path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
34150026/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
train.head() | code |
73070475/cell_4 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plt.show() | code |
73070475/cell_6 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plat2 = cv2.imread('/kaggle/input/plat-nomer/3.png', 50)
edges = cv2.Canny(plat2, 100, 200)
(plt.subplot(121), plt.imshow(plat2, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg')
edges = cv2.Canny(plat1, 50, 255, L2gradient=False)
plt.imshow(edges, cmap='gray')
plt.show() | code |
73070475/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73070475/cell_7 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plat2 = cv2.imread('/kaggle/input/plat-nomer/3.png', 50)
edges = cv2.Canny(plat2, 100, 200)
(plt.subplot(121), plt.imshow(plat2, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg')
edges = cv2.Canny(plat1, 50, 255, L2gradient=False)
plat2 = cv2.imread('/kaggle/input/plat-nomer/3.png')
edges = cv2.Canny(plat2, 50, 255, L2gradient=False)
plt.imshow(edges, cmap='gray')
plt.show() | code |
73070475/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plat2 = cv2.imread('/kaggle/input/plat-nomer/3.png', 50)
edges = cv2.Canny(plat2, 100, 200)
(plt.subplot(121), plt.imshow(plat2, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edge'), plt.xticks([]), plt.yticks([]))
plt.show() | code |
325101/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y) | code |
325101/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y)
gb.score(test, test_y) | code |
325101/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
df = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
df.columns | code |
325101/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y)
sv.score(test, test_y) | code |
325101/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y)
rf.score(test, test_y) | code |
106214685/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
sns.set_theme()
sns.set(rc={'figure.figsize': (6, 4)})
data.isnull().sum()
data.duplicated().sum()
data.loc[data.duplicated(), :] | code |
106214685/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique() | code |
106214685/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS'] | code |
106214685/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
sns.set_theme()
sns.set(rc={'figure.figsize': (6, 4)})
data.isnull().sum()
data.duplicated().sum()
data.loc[data.duplicated(), :]
nv = []
for i in data.columns:
if data[i].dtypes != 'object':
nv.append(i)
print(nv) | code |
106214685/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
sns.set_theme()
sns.set(rc={'figure.figsize': (6, 4)})
data.isnull().sum()
data.duplicated().sum() | code |
106214685/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns | code |
106214685/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique() | code |
106214685/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106214685/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique() | code |
106214685/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
sns.set_theme()
sns.set(rc={'figure.figsize': (6, 4)})
data.isnull().sum() | code |
106214685/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean() | code |
106214685/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
data.info() | code |
106214685/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
sns.set_theme()
sns.set(rc={'figure.figsize': (6, 4)})
print(f'This DataSet Contains {data.shape[0]} rows & {data.shape[1]} columns.') | code |
106214685/cell_3 | [
"text_plain_output_1.png"
] | import os
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings('ignore')
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
import pandas as pd
import datetime
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.cm as cm
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())
import seaborn as sns
sns.set(style='ticks', color_codes=True, font_scale=1.5)
color = sns.color_palette()
sns.set_style('darkgrid')
from mpl_toolkits.mplot3d import Axes3D
import plotly as py
import plotly.graph_objs as go
py.offline.init_notebook_mode()
from scipy import stats
from scipy.stats import skew, norm, probplot, boxcox
from sklearn import preprocessing
import math
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from sklearn.compose import make_column_transformer
from sklearn import set_config
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
import seaborn as dg | code |
106214685/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum()
data.CustomerID.unique()
data.describe() | code |
106214685/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
data.Itemname.unique()
data.CustomerID.unique()
data.fillna({'Itemname': 'no', 'CustomerID': 'Others'}, inplace=True)
data.isna().sum() | code |
106214685/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.head() | code |
32068762/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T
df = population_raw.copy()
df = df.rename({'Province.State': 'Province', 'Country.Region': 'Country'}, axis=1)
cols = ['Country', 'Province', 'Population']
df = df.loc[:, cols].fillna('-')
df.loc[df['Country'] == df['Province'], 'Province'] = '-'
_total_df = df.loc[df['Province'] != '-', :].groupby('Country').sum()
_total_df = _total_df.reset_index().assign(Province='-')
df = pd.concat([df, _total_df], axis=0, sort=True)
df = df.drop_duplicates(subset=['Country', 'Province'], keep='first')
global_value = df.loc[df['Province'] == '-', 'Population'].sum()
df = df.append(pd.Series(['Global', '-', global_value], index=cols), ignore_index=True)
df = df.sort_values('Population', ascending=False).reset_index(drop=True)
df = df.loc[:, cols]
population_df = df.copy()
df = population_df.loc[population_df['Province'] == '-', :]
population_dict = df.set_index('Country').to_dict()['Population']
population_dict
pyramid_csv_list = list()
for dirname, _, filenames in os.walk('/kaggle/input/population-pyramid-2019/'):
for filename in filenames:
name = os.path.join(dirname, filename)
df = pd.read_csv(name)
df['Country'], df['Year'], _ = filename.replace('.', '-').split('-')
pyramid_csv_list.append(df)
pyramid_raw = pd.concat(pyramid_csv_list, sort=True)
pyramid_raw.head() | code |
32068762/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068762/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T
df = population_raw.copy()
df = df.rename({'Province.State': 'Province', 'Country.Region': 'Country'}, axis=1)
cols = ['Country', 'Province', 'Population']
df = df.loc[:, cols].fillna('-')
df.loc[df['Country'] == df['Province'], 'Province'] = '-'
_total_df = df.loc[df['Province'] != '-', :].groupby('Country').sum()
_total_df = _total_df.reset_index().assign(Province='-')
df = pd.concat([df, _total_df], axis=0, sort=True)
df = df.drop_duplicates(subset=['Country', 'Province'], keep='first')
global_value = df.loc[df['Province'] == '-', 'Population'].sum()
df = df.append(pd.Series(['Global', '-', global_value], index=cols), ignore_index=True)
df = df.sort_values('Population', ascending=False).reset_index(drop=True)
df = df.loc[:, cols]
population_df = df.copy()
df = population_df.loc[population_df['Province'] == '-', :]
population_dict = df.set_index('Country').to_dict()['Population']
population_dict
pyramid_csv_list = list()
for dirname, _, filenames in os.walk('/kaggle/input/population-pyramid-2019/'):
for filename in filenames:
name = os.path.join(dirname, filename)
df = pd.read_csv(name)
df['Country'], df['Year'], _ = filename.replace('.', '-').split('-')
pyramid_csv_list.append(df)
pyramid_raw = pd.concat(pyramid_csv_list, sort=True)
df = pyramid_raw.copy()
df['Country'] = df['Country'].replace({'United States of America': 'US', 'United Kingdom': 'UK'})
_male = [349432556, 342927576, 331497486, 316642222, 308286775, 306059387, 309236984, 276447037, 249389688, 241232876, 222609691, 192215395, 157180267, 128939392, 87185982, 54754941, 33648953, 15756942, 5327866, 1077791, 124144]
_female = [328509234, 321511867, 309769906, 295553758, 289100903, 288632766, 296293748, 268371754, 244399176, 238133281, 223162982, 195633743, 164961323, 140704320, 101491347, 69026831, 48281201, 26429329, 11352182, 3055845, 449279]
_df = pd.DataFrame({'Age': df['Age'].unique(), 'Country': 'Global', 'F': _female, 'M': _male, 'Year': 2019})
df = pd.concat([df, _df], axis=0, ignore_index=True, sort=True)
_male = [307116, 304759, 296771, 270840, 291723, 376952, 343311, 315086, 312017, 336452, 342117, 306949, 279609, 265511, 273061, 195029, 113166, 61775, 26170, 6768, 415]
_female = [290553, 288817, 280944, 257677, 274760, 361526, 330153, 300752, 301288, 327453, 331458, 300084, 280009, 272149, 286879, 212480, 143654, 97633, 52624, 18130, 1771]
_df = pd.DataFrame({'Age': df['Age'].unique(), 'Country': 'Sweden', 'F': _female, 'M': _male, 'Year': 2019})
df = pd.concat([df, _df], axis=0, ignore_index=True, sort=True)
_male = [5534962, 5820604, 5538414, 5383822, 5149849, 4710777, 4061897, 3581091, 3237426, 2832825, 2482953, 2015857, 1556935, 1082875, 668107, 364200, 199400, 73508, 17327, 3035, 208]
_female = [5240508, 5541514, 5273495, 5029137, 4896316, 4589506, 3982681, 3544279, 3191565, 2825286, 2521463, 2112380, 1714689, 1285782, 895866, 567282, 360751, 155294, 57969, 13376, 1411]
_df = pd.DataFrame({'Age': df['Age'].unique(), 'Country': 'Philippines', 'F': _female, 'M': _male, 'Year': 2019})
df = pd.concat([df, _df], axis=0, ignore_index=True, sort=True)
df['Population'] = df['F'] + df['M']
df = df.pivot_table(index='Age', columns=['Country'], values='Population', aggfunc='last')
df = df.astype(np.int64).reset_index().rename({'Age': 'Age_bin'}, axis=1)
series = df['Age_bin'].str.replace('+', '-122')
df[['Age_first', 'Age_last']] = series.str.split('-', expand=True).astype(np.int64)
df = df.drop('Age_bin', axis=1)
series = df['Age_last']
df = df.apply(lambda x: x[:-2] / (x[-1] - x[-2] + 1), axis=1)
df['Age'] = series
df = pd.merge(df, pd.DataFrame({'Age': np.arange(0, 123, 1)}), on='Age', how='right', sort=True)
df = df.fillna(method='bfill').astype(np.int64)
df = df.set_index('Age')
pyramid_df = df.copy()
pyramid_df | code |
32068762/cell_2 | [
"text_plain_output_1.png"
] | from datetime import datetime
from datetime import datetime
time_format = '%d%b%Y %H:%M'
datetime.now().strftime(time_format) | code |
32068762/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T
df = population_raw.copy()
df = df.rename({'Province.State': 'Province', 'Country.Region': 'Country'}, axis=1)
cols = ['Country', 'Province', 'Population']
df = df.loc[:, cols].fillna('-')
df.loc[df['Country'] == df['Province'], 'Province'] = '-'
_total_df = df.loc[df['Province'] != '-', :].groupby('Country').sum()
_total_df = _total_df.reset_index().assign(Province='-')
df = pd.concat([df, _total_df], axis=0, sort=True)
df = df.drop_duplicates(subset=['Country', 'Province'], keep='first')
global_value = df.loc[df['Province'] == '-', 'Population'].sum()
df = df.append(pd.Series(['Global', '-', global_value], index=cols), ignore_index=True)
df = df.sort_values('Population', ascending=False).reset_index(drop=True)
df = df.loc[:, cols]
population_df = df.copy()
df = population_df.loc[population_df['Province'] == '-', :]
population_dict = df.set_index('Country').to_dict()['Population']
population_dict | code |
32068762/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T
df = population_raw.copy()
df = df.rename({'Province.State': 'Province', 'Country.Region': 'Country'}, axis=1)
cols = ['Country', 'Province', 'Population']
df = df.loc[:, cols].fillna('-')
df.loc[df['Country'] == df['Province'], 'Province'] = '-'
_total_df = df.loc[df['Province'] != '-', :].groupby('Country').sum()
_total_df = _total_df.reset_index().assign(Province='-')
df = pd.concat([df, _total_df], axis=0, sort=True)
df = df.drop_duplicates(subset=['Country', 'Province'], keep='first')
global_value = df.loc[df['Province'] == '-', 'Population'].sum()
df = df.append(pd.Series(['Global', '-', global_value], index=cols), ignore_index=True)
df = df.sort_values('Population', ascending=False).reset_index(drop=True)
df = df.loc[:, cols]
population_df = df.copy()
population_df.head() | code |
32068762/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
population_raw.head() | code |
32068762/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T | code |
32068762/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (9, 6)
pd.set_option('display.max_colwidth', 1000)
population_raw = pd.read_csv('/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv')
pd.DataFrame(population_raw.isnull().sum()).T
df = population_raw.copy()
df = df.rename({'Province.State': 'Province', 'Country.Region': 'Country'}, axis=1)
cols = ['Country', 'Province', 'Population']
df = df.loc[:, cols].fillna('-')
df.loc[df['Country'] == df['Province'], 'Province'] = '-'
_total_df = df.loc[df['Province'] != '-', :].groupby('Country').sum()
_total_df = _total_df.reset_index().assign(Province='-')
df = pd.concat([df, _total_df], axis=0, sort=True)
df = df.drop_duplicates(subset=['Country', 'Province'], keep='first')
global_value = df.loc[df['Province'] == '-', 'Population'].sum()
df = df.append(pd.Series(['Global', '-', global_value], index=cols), ignore_index=True)
df = df.sort_values('Population', ascending=False).reset_index(drop=True)
df = df.loc[:, cols]
population_df = df.copy()
df = population_df.loc[population_df['Province'] == '-', :]
population_dict = df.set_index('Country').to_dict()['Population']
population_dict
pyramid_csv_list = list()
for dirname, _, filenames in os.walk('/kaggle/input/population-pyramid-2019/'):
for filename in filenames:
name = os.path.join(dirname, filename)
df = pd.read_csv(name)
df['Country'], df['Year'], _ = filename.replace('.', '-').split('-')
pyramid_csv_list.append(df)
pyramid_raw = pd.concat(pyramid_csv_list, sort=True)
pyramid_raw['Country'].unique() | code |
17108052/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes | code |
17108052/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train['Price'].describe() | code |
17108052/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
print(train_csv.columns) | code |
17108052/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI', 'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP', 'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up', 'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM', 'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D', 'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE', 'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price']
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=0.8, square=True) | code |
17108052/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
sns.distplot(train['Price']) | code |
17108052/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
#box plot overallqual/saleprice
var = 'Fuel_Type'
data = pd.concat([train['Price'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="Price", data=data)
fig.axis(ymin=0, ymax=160);
#correlation matrix
corrmat = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True);
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 36].index, 1)
train = train.drop(train.loc[train['Engine'].isnull()].index)
train = train.drop(train.loc[train['Mileage'].isnull()].index)
train.isnull().sum().max() | code |
17108052/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
print('Skewness: %f' % train['Price'].skew())
print('Kurtosis: %f' % train['Price'].kurt()) | code |
17108052/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
#box plot overallqual/saleprice
var = 'Fuel_Type'
data = pd.concat([train['Price'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="Price", data=data)
fig.axis(ymin=0, ymax=160);
corrmat = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=0.8, square=True) | code |
17108052/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
#box plot overallqual/saleprice
var = 'Fuel_Type'
data = pd.concat([train['Price'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="Price", data=data)
fig.axis(ymin=0, ymax=160);
#correlation matrix
corrmat = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True);
k = 7
cols = corrmat.nlargest(k, 'Price')['Price'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show() | code |
17108052/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
#box plot overallqual/saleprice
var = 'Fuel_Type'
data = pd.concat([train['Price'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="Price", data=data)
fig.axis(ymin=0, ymax=160);
#correlation matrix
corrmat = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True);
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(20) | code |
17108052/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
var = 'Fuel_Type'
data = pd.concat([train['Price'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y='Price', data=data)
fig.axis(ymin=0, ymax=160) | code |
17108052/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
data.plot.scatter(x=var, y='Price', ylim=(0, 702)) | code |
17108052/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
train.dtypes
var = 'Aroon Up'
data = pd.concat([train['Price'], train[var]], axis=1)
corcolm = ['SMA', 'MACD', 'MACD_Hist', 'SlowD', 'FastK', 'RSI',
'FatD', 'FatK', 'WILLR', 'ADX', 'ADXR', 'PPO', 'MOM', 'BOP',
'CCI', 'CMO', 'ROC', 'ROCR', 'Aroon Down', 'Aroon Up',
'MFI', 'TRIX', 'ULTOSC', 'DX', 'MINUS_DI', 'PLUS_DI', 'MINUS_DM',
'PLUS_DM', 'Real Lower Band', 'MIDPOINT', 'MIDPRICE', 'SAR', 'ATR', 'Chaikin A/D',
'ADOSC', 'OBV', 'HT_TRENDLINE', 'LEAD SINE', 'SINE', 'TRENDMODE',
'DCPERIOD', 'HT_DCPHASE', 'PHASE', 'QUADRATURE', 'Price'];
#correlation matrix
corrmat = train[corcolm].corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(corrmat, vmax=.8, square=True);
train = train[corcolm]
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(45) | code |
17108052/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from sklearn.metrics import accuracy_score
import json
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from collections import Counter
from sklearn.preprocessing import LabelEncoder, scale
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.cross_decomposition import PLSRegression, PLSSVD
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import torch.utils.data
from sklearn.model_selection import train_test_split
import torch
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 500)
train_csv = pd.read_csv('../input/train.csv', keep_default_na=False)
test_csv = pd.read_csv('../input/test.csv', keep_default_na=False)
def preprocess_data(dataset):
dataset = dataset.replace('NaN', '')
for col in list(dataset.columns):
if col != 'Company ' and col != 'Date':
dataset[col] = pd.to_numeric(dataset[col])
dataset = dataset.drop(['ID'], axis=1)
return dataset
train = preprocess_data(train_csv)
test = preprocess_data(test_csv)
display(train.head()) | code |
73063106/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer | code |
73063106/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.head(5) | code |
73063106/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True) | code |
73063106/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape | code |
73063106/cell_34 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df_customer)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
dff = scaled_features
from sklearn.cluster import KMeans
wcss = []
for k in range(2, 11):
kmeanModel = KMeans(n_clusters=k, init='k-means++')
kmeanModel.fit(dff)
wcss.append(kmeanModel.inertia_)
silhouette_coefficients = []
for k in range(2, 11):
kmeans = KMeans(n_clusters=k, init='random', n_init=10, max_iter=300, random_state=42)
kmeans.fit(dff)
score = silhouette_score(scaled_features, kmeans.labels_)
silhouette_coefficients.append(score)
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=7, random_state=0)
df_customer['cluster'] = kmeans.fit_predict(df_customer[['food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']]) | code |
73063106/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1) | code |
73063106/cell_33 | [
"image_output_1.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df_customer)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
dff = scaled_features
from sklearn.cluster import KMeans
wcss = []
for k in range(2, 11):
kmeanModel = KMeans(n_clusters=k, init='k-means++')
kmeanModel.fit(dff)
wcss.append(kmeanModel.inertia_)
silhouette_coefficients = []
for k in range(2, 11):
kmeans = KMeans(n_clusters=k, init='random', n_init=10, max_iter=300, random_state=42)
kmeans.fit(dff)
score = silhouette_score(scaled_features, kmeans.labels_)
silhouette_coefficients.append(score)
from kneed import KneeLocator
kl = KneeLocator(x=range(2, 11), y=silhouette_coefficients, curve='convex', direction='decreasing')
kl.elbow
kl = KneeLocator(x=range(2, 11), y=wcss, curve='convex', direction='decreasing')
kl.elbow | code |
73063106/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum() | code |
73063106/cell_29 | [
"text_html_output_1.png"
] | pip install kneed | code |
73063106/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
df_customer | code |
73063106/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df_customer)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
dff = scaled_features
from sklearn.cluster import KMeans
wcss = []
for k in range(2, 11):
kmeanModel = KMeans(n_clusters=k, init='k-means++')
kmeanModel.fit(dff)
wcss.append(kmeanModel.inertia_)
plt.figure(figsize=(16, 8))
plt.plot(range(2, 11), wcss, 'bx-')
plt.xlabel('k')
plt.ylabel('Distortion')
plt.title('The Elbow Method showing the optimal k')
plt.show() | code |
73063106/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
temp_df = df_customer[df_customer.cluster == 1]
dff = df_customer.drop('cluster', axis=1)
df_customer | code |
73063106/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean() | code |
73063106/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73063106/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.info() | code |
73063106/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates()) | code |
73063106/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df_customer)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
dff = scaled_features
from sklearn.cluster import KMeans
wcss = []
for k in range(2, 11):
kmeanModel = KMeans(n_clusters=k, init='k-means++')
kmeanModel.fit(dff)
wcss.append(kmeanModel.inertia_)
silhouette_coefficients = []
for k in range(2, 11):
kmeans = KMeans(n_clusters=k, init='random', n_init=10, max_iter=300, random_state=42)
kmeans.fit(dff)
score = silhouette_score(scaled_features, kmeans.labels_)
silhouette_coefficients.append(score)
plt.style.use('fivethirtyeight')
plt.plot(range(2, 11), silhouette_coefficients)
plt.xticks(range(2, 11))
plt.xlabel('Number of Clusters')
plt.ylabel('Silhouette Coefficient')
plt.show() | code |
73063106/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
df_weekends = df[(df['weekday'] == 7) | (df['weekday'] == 6)]
df_weekdays = df[(df['weekday'] != 7) & (df['weekday'] != 6)]
df_weekdays.shape[0] | code |
73063106/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) | code |
73063106/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df_customer)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
dff = scaled_features
from sklearn.cluster import KMeans
wcss = []
for k in range(2, 11):
kmeanModel = KMeans(n_clusters=k, init='k-means++')
kmeanModel.fit(dff)
wcss.append(kmeanModel.inertia_)
silhouette_coefficients = []
for k in range(2, 11):
kmeans = KMeans(n_clusters=k, init='random', n_init=10, max_iter=300, random_state=42)
kmeans.fit(dff)
score = silhouette_score(scaled_features, kmeans.labels_)
silhouette_coefficients.append(score)
from kneed import KneeLocator
kl = KneeLocator(x=range(2, 11), y=silhouette_coefficients, curve='convex', direction='decreasing')
kl.elbow | code |
73063106/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
df_weekends = df[(df['weekday'] == 7) | (df['weekday'] == 6)]
df_weekdays = df[(df['weekday'] != 7) & (df['weekday'] != 6)]
df_weekends.shape[0] | code |
73063106/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.describe() | code |
73063106/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()] | code |
73063106/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns | code |
73063106/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday', 'hour', 'food_percent', 'fresh_percent', 'drinks_percent', 'home_percent', 'beauty_percent', 'health_percent', 'baby_percent', 'pets_percent']
df.discount_percent.mean()
df[df['total_items'] == df.total_items.max()]
len(df) - len(df.drop_duplicates())
df_customer = df.drop_duplicates('customer', keep='first')
df_customer.drop(['customer', 'order'], inplace=True, axis=1)
import matplotlib.pyplot as plt
df_corr = df_customer.corr()
fig, ax = plt.subplots(figsize=(16, 16))
ax = sns.heatmap(df_corr, annot=True)
u_labels = df_customer['cluster'].unique()
print(u_labels) | code |
34133142/cell_20 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnbdm/train.csv')
test = pd.read_csv('/kaggle/input/airbnbdm/test.csv')
submission = pd.DataFrame()
submission['Id'] = test['id'].copy()
df.columns
verification_methods = ['phone', 'email', 'reviews', 'government_id', 'jumio', 'offline_government_id', 'kba', 'facebook', 'selfie', 'work_email', 'identity_manual', 'google', 'manual_offline', 'manual_online', 'sent_id', 'None', 'weibo', 'zhima_selfie', 'sesame_offline', 'sesame']
def clean(df, test):
cols_to_drop = ['id', 'name', 'summary', 'space', 'description', 'experiences_offered', 'neighborhood_overview', 'notes', 'access', 'interaction', 'house_rules', 'host_id', 'host_name', 'host_location', 'host_about', 'host_neighbourhood', 'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'country_code', 'square_feet', 'host_listings_count', 'reviews_per_month', 'first_review', 'last_review', 'beds', 'host_verifications']
df['extra_people'] = df['extra_people'].str.strip('$').apply(lambda x: float(x))
test['extra_people'] = test['extra_people'].str.strip('$').apply(lambda x: float(x))
df['host_acceptance_rate'] = df['host_acceptance_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
test['host_acceptance_rate'] = test['host_acceptance_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
df['host_response_rate'] = df['host_response_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
test['host_response_rate'] = test['host_response_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
df['host_in_neighbourhood'] = (df['host_neighbourhood'] == df['neighbourhood_group_cleansed']).fillna(False)
test['host_in_neighbourhood'] = (test['host_neighbourhood'] == test['neighbourhood_group_cleansed']).fillna(False)
df['host_verifications'].str.strip('[').str.strip(']')[df['host_verifications'].str.strip('[').str.strip(']') == ''] = '[None]'
test['host_verifications'].str.strip('[').str.strip(']')[test['host_verifications'].str.strip('[').str.strip(']') == ''] = '[None]'
for method in verification_methods:
df['is_' + method] = df['host_verifications'].str.contains(method)
test['is_' + method] = test['host_verifications'].str.contains(method)
def helper(x):
if pd.isnull(x):
return 0
if x == 'within an hour':
return 4
if x == 'within a few hours':
return 3
if x == 'within a day':
return 2
if x == 'a few days or more':
return 1
return 0
df['host_response_time'] = df['host_response_time'].apply(lambda x: helper(x))
test['host_response_time'] = test['host_response_time'].apply(lambda x: helper(x))
stored_ix = df[~df['description'].isnull()].index
stored_ix2 = test[~test['description'].isnull()].index
num_non_null_df = df[~df['description'].isnull()].shape[0]
stemmer = SnowballStemmer('english')
def preprocess_text(corpus):
"""Takes a corpus in list format and applies basic preprocessing steps of word tokenization,
removing of english stop words, lower case and lemmatization."""
processed_corpus = []
english_words = set(nltk.corpus.words.words())
english_stopwords = set(stopwords.words('english'))
wordnet_lemmatizer = WordNetLemmatizer()
tokenizer = RegexpTokenizer('[\\w|!]+')
for row in corpus:
word_tokens = tokenizer.tokenize(row)
word_tokens_lower = [t.lower() for t in word_tokens]
word_tokens_lower_english = [t for t in word_tokens_lower if t in english_words or not t.isalpha()]
word_tokens_no_stops = [t for t in word_tokens_lower_english if not t in english_stopwords]
word_tokens_no_stops_lemmatized = [wordnet_lemmatizer.lemmatize(t) for t in word_tokens_no_stops]
processed_corpus.append(word_tokens_no_stops_lemmatized)
return processed_corpus
stemmed_stopped = preprocess_text(df['description'].dropna().append(test['description'].dropna(), ignore_index=True))
dictionary = gensim.corpora.Dictionary(stemmed_stopped)
dictionary.filter_extremes(no_below=15, keep_n=100000)
bow_corpus = [dictionary.doc2bow(doc) for doc in stemmed_stopped]
lda_model = gensim.models.LdaMulticore(bow_corpus, num_topics=3, id2word=dictionary, passes=2, workers=4)
results = {0: [], 1: [], 2: []}
for doc in bow_corpus[0:num_non_null_df]:
done = []
for i in lda_model[doc]:
results[i[0]].append(i[1])
done.append(i[0])
if 0 not in done:
results[0].append(0)
if 1 not in done:
results[1].append(0)
if 2 not in done:
results[2].append(0)
labels = pd.DataFrame(results)
labels.index = stored_ix
labels.columns = ['Cluster0', 'Cluster1', 'Cluster2']
df = df.join(labels)
results = {0: [], 1: [], 2: []}
for doc in bow_corpus[num_non_null_df:]:
done = []
for i in lda_model[doc]:
results[i[0]].append(i[1])
done.append(i[0])
if 0 not in done:
results[0].append(0)
if 1 not in done:
results[1].append(0)
if 2 not in done:
results[2].append(0)
labels = pd.DataFrame(results)
labels.index = stored_ix2
labels.columns = ['Cluster0', 'Cluster1', 'Cluster2']
test = test.join(labels)
df['listing_time'] = df['number_of_reviews'] / df['reviews_per_month']
df['listing_time'] = df['listing_time'].fillna(0)
df = df.drop(cols_to_drop, axis=1)
df['is_train'] = df['transit'].str.lower().str.contains('train')
df['is_bus'] = df['transit'].str.lower().str.contains('bus')
df['is_subway'] = df['transit'].str.lower().str.contains('subway')
df['is_cab'] = df['transit'].str.lower().str.contains('cab') | df['transit'].str.lower().str.contains('car') | df['transit'].str.lower().str.contains('uber') | df['transit'].str.lower().str.contains('taxi')
df['is_metro'] = df['transit'].str.lower().str.contains('metro')
df['is_walk'] = df['transit'].str.lower().str.contains('walk')
df['is_wifi'] = df['amenities'].str.lower().str.contains('wifi') | df['amenities'].str.lower().str.contains('internet')
df['is_kitchen'] = df['amenities'].str.lower().str.contains('kitchen')
df['is_heating'] = df['amenities'].str.lower().str.contains('heat')
df['is_ac'] = df['amenities'].str.lower().str.contains('air conditioning')
df['is_washer'] = df['amenities'].str.lower().str.contains('washer') | df['amenities'].str.lower().str.contains('dryer') | df['amenities'].str.lower().str.contains('dishwasher')
df['is_tv'] = df['amenities'].str.lower().str.contains('tv')
df['is_gym'] = df['amenities'].str.lower().str.contains('gym')
df['is_pets'] = df['amenities'].str.lower().str.contains('pet')
df['is_balcony'] = df['amenities'].str.lower().str.contains('balcony')
df['is_linen'] = df['amenities'].str.lower().str.contains('linen')
df['is_breakfast'] = df['amenities'].str.lower().str.contains('breakfast')
df['is_coffee'] = df['amenities'].str.lower().str.contains('coffee')
df['is_cooking'] = df['amenities'].str.lower().str.contains('cooking')
df['is_pool'] = df['amenities'].str.lower().str.contains('pool')
df['amenities'] = df['amenities'].str.strip('{').str.strip('}').str.split(',').apply(lambda x: len(x) if x[0] != '' else 0)
df['host_is_superhost'] = df['host_is_superhost'].replace({'f': False, 't': True})
df = df.drop(['transit'], axis=1)
for col in df[pd.Series(df.columns)[pd.Series(df.columns).str.contains('review')].to_list()].columns:
to_fill = pd.Series(df[col].sample(df[col].isnull().sum()))
to_fill.index = df[df[col].isnull()].index
df[col] = df[col].fillna(to_fill)
df[df.columns[df.columns.str.contains('^is')]] = df[df.columns[df.columns.str.contains('^is')]].fillna('False')
df = df.fillna(0)
df = df.reset_index(drop=True)
test['listing_time'] = test['number_of_reviews'] / test['reviews_per_month']
test['listing_time'] = test['listing_time'].fillna(0)
test = test.drop(cols_to_drop, axis=1)
test['is_train'] = test['transit'].str.lower().str.contains('train')
test['is_bus'] = test['transit'].str.lower().str.contains('bus')
test['is_subway'] = test['transit'].str.lower().str.contains('subway')
test['is_cab'] = test['transit'].str.lower().str.contains('cab') | test['transit'].str.lower().str.contains('car') | test['transit'].str.lower().str.contains('uber') | test['transit'].str.lower().str.contains('taxi')
test['is_metro'] = test['transit'].str.lower().str.contains('metro')
test['is_walk'] = test['transit'].str.lower().str.contains('walk')
test['is_wifi'] = test['amenities'].str.lower().str.contains('wifi') | test['amenities'].str.lower().str.contains('internet')
test['is_kitchen'] = test['amenities'].str.lower().str.contains('kitchen')
test['is_heating'] = test['amenities'].str.lower().str.contains('heat')
test['is_ac'] = test['amenities'].str.lower().str.contains('air conditioning')
test['is_washer'] = test['amenities'].str.lower().str.contains('washer') | test['amenities'].str.lower().str.contains('dryer') | test['amenities'].str.lower().str.contains('dishwasher')
test['is_tv'] = test['amenities'].str.lower().str.contains('tv')
test['is_gym'] = test['amenities'].str.lower().str.contains('gym')
test['is_pets'] = test['amenities'].str.lower().str.contains('pet')
test['is_balcony'] = test['amenities'].str.lower().str.contains('balcony')
test['is_linen'] = test['amenities'].str.lower().str.contains('linen')
test['is_breakfast'] = test['amenities'].str.lower().str.contains('breakfast')
test['is_coffee'] = test['amenities'].str.lower().str.contains('coffee')
test['is_cooking'] = test['amenities'].str.lower().str.contains('cooking')
test['is_pool'] = test['amenities'].str.lower().str.contains('pool')
test['amenities'] = test['amenities'].str.strip('{').str.strip('}').str.split(',').apply(lambda x: len(x) if x[0] != '' else 0)
test['host_is_superhost'] = test['host_is_superhost'].replace({'f': False, 't': True})
test = test.drop(['transit'], axis=1)
for col in test[pd.Series(test.columns)[pd.Series(test.columns).str.contains('review')].to_list()].columns:
to_fill = pd.Series(test[col].sample(test[col].isnull().sum()))
to_fill.index = test[test[col].isnull()].index
test[col] = test[col].fillna(to_fill)
test[test.columns[test.columns.str.contains('^is')]] = test[test.columns[test.columns.str.contains('^is')]].fillna('False')
test = test.fillna(0)
df['bedrooms'] = df['bedrooms'].apply(lambda x: str(x))
df['bathrooms'] = df['bathrooms'].apply(lambda x: str(x))
test['bedrooms'] = test['bedrooms'].apply(lambda x: str(x))
test['bathrooms'] = test['bathrooms'].apply(lambda x: str(x))
test = test.reset_index(drop=True)
return (df.replace({False: 0, True: 1, 'False': 0, 'True': 1, 'f': 0, 't': 1}), test.replace({False: 0, True: 1, 'False': 0, 'True': 1, 'f': 0, 't': 1}))
df.columns
df.shape
scores = []
for i in range(20):
time_features = ['host_since']
time_converter = Pipeline(steps=[('ft', FunctionTransformer(lambda x: (2020 - pd.to_datetime(x['host_since']).apply(lambda u: u.year)).values.reshape(-1, 1)))])
ohe_features = ['neighbourhood_cleansed', 'property_type', 'room_type', 'bed_type', 'cancellation_policy', 'bathrooms', 'bedrooms', 'market', 'country']
ohe_converter = Pipeline(steps=[('ohe', OneHotEncoder(handle_unknown='ignore'))])
preproc = ColumnTransformer(transformers=[('time', time_converter, time_features)], remainder='passthrough')
catt_features = [df.drop(['price'], axis=1).columns.get_loc(col) for col in ohe_features]
pl = Pipeline(steps=[('preprocessor', preproc), ('regressor', CatBoostRegressor(cat_features=catt_features, silent=True))])
break
submission['Predicted'] = pd.Series(pl.predict(test))
preprocessed = pl['preprocessor'].fit_transform(df.drop('price', axis=1))
preprocessed = preprocessed
preprocessed | code |
34133142/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import ElasticNet
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error as rmse
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from catboost import CatBoostRegressor
import gensim
from gensim import corpora, models
from gensim.utils import simple_preprocess
from gensim.parsing.preprocessing import STOPWORDS
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.stem.porter import *
import numpy as np
np.random.seed(2018)
import nltk
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
import ast
import os
for dirname, _, filenames in os.walk('/kaggle/input/airbnbdm'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34133142/cell_7 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnbdm/train.csv')
test = pd.read_csv('/kaggle/input/airbnbdm/test.csv')
submission = pd.DataFrame()
submission['Id'] = test['id'].copy()
df.columns
verification_methods = ['phone', 'email', 'reviews', 'government_id', 'jumio', 'offline_government_id', 'kba', 'facebook', 'selfie', 'work_email', 'identity_manual', 'google', 'manual_offline', 'manual_online', 'sent_id', 'None', 'weibo', 'zhima_selfie', 'sesame_offline', 'sesame']
def clean(df, test):
cols_to_drop = ['id', 'name', 'summary', 'space', 'description', 'experiences_offered', 'neighborhood_overview', 'notes', 'access', 'interaction', 'house_rules', 'host_id', 'host_name', 'host_location', 'host_about', 'host_neighbourhood', 'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'country_code', 'square_feet', 'host_listings_count', 'reviews_per_month', 'first_review', 'last_review', 'beds', 'host_verifications']
df['extra_people'] = df['extra_people'].str.strip('$').apply(lambda x: float(x))
test['extra_people'] = test['extra_people'].str.strip('$').apply(lambda x: float(x))
df['host_acceptance_rate'] = df['host_acceptance_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
test['host_acceptance_rate'] = test['host_acceptance_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
df['host_response_rate'] = df['host_response_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
test['host_response_rate'] = test['host_response_rate'].fillna('0%').str.strip('%').apply(lambda x: float(x))
df['host_in_neighbourhood'] = (df['host_neighbourhood'] == df['neighbourhood_group_cleansed']).fillna(False)
test['host_in_neighbourhood'] = (test['host_neighbourhood'] == test['neighbourhood_group_cleansed']).fillna(False)
df['host_verifications'].str.strip('[').str.strip(']')[df['host_verifications'].str.strip('[').str.strip(']') == ''] = '[None]'
test['host_verifications'].str.strip('[').str.strip(']')[test['host_verifications'].str.strip('[').str.strip(']') == ''] = '[None]'
for method in verification_methods:
df['is_' + method] = df['host_verifications'].str.contains(method)
test['is_' + method] = test['host_verifications'].str.contains(method)
def helper(x):
if pd.isnull(x):
return 0
if x == 'within an hour':
return 4
if x == 'within a few hours':
return 3
if x == 'within a day':
return 2
if x == 'a few days or more':
return 1
return 0
df['host_response_time'] = df['host_response_time'].apply(lambda x: helper(x))
test['host_response_time'] = test['host_response_time'].apply(lambda x: helper(x))
stored_ix = df[~df['description'].isnull()].index
stored_ix2 = test[~test['description'].isnull()].index
num_non_null_df = df[~df['description'].isnull()].shape[0]
stemmer = SnowballStemmer('english')
def preprocess_text(corpus):
"""Takes a corpus in list format and applies basic preprocessing steps of word tokenization,
removing of english stop words, lower case and lemmatization."""
processed_corpus = []
english_words = set(nltk.corpus.words.words())
english_stopwords = set(stopwords.words('english'))
wordnet_lemmatizer = WordNetLemmatizer()
tokenizer = RegexpTokenizer('[\\w|!]+')
for row in corpus:
word_tokens = tokenizer.tokenize(row)
word_tokens_lower = [t.lower() for t in word_tokens]
word_tokens_lower_english = [t for t in word_tokens_lower if t in english_words or not t.isalpha()]
word_tokens_no_stops = [t for t in word_tokens_lower_english if not t in english_stopwords]
word_tokens_no_stops_lemmatized = [wordnet_lemmatizer.lemmatize(t) for t in word_tokens_no_stops]
processed_corpus.append(word_tokens_no_stops_lemmatized)
return processed_corpus
stemmed_stopped = preprocess_text(df['description'].dropna().append(test['description'].dropna(), ignore_index=True))
dictionary = gensim.corpora.Dictionary(stemmed_stopped)
dictionary.filter_extremes(no_below=15, keep_n=100000)
bow_corpus = [dictionary.doc2bow(doc) for doc in stemmed_stopped]
lda_model = gensim.models.LdaMulticore(bow_corpus, num_topics=3, id2word=dictionary, passes=2, workers=4)
results = {0: [], 1: [], 2: []}
for doc in bow_corpus[0:num_non_null_df]:
done = []
for i in lda_model[doc]:
results[i[0]].append(i[1])
done.append(i[0])
if 0 not in done:
results[0].append(0)
if 1 not in done:
results[1].append(0)
if 2 not in done:
results[2].append(0)
labels = pd.DataFrame(results)
labels.index = stored_ix
labels.columns = ['Cluster0', 'Cluster1', 'Cluster2']
df = df.join(labels)
results = {0: [], 1: [], 2: []}
for doc in bow_corpus[num_non_null_df:]:
done = []
for i in lda_model[doc]:
results[i[0]].append(i[1])
done.append(i[0])
if 0 not in done:
results[0].append(0)
if 1 not in done:
results[1].append(0)
if 2 not in done:
results[2].append(0)
labels = pd.DataFrame(results)
labels.index = stored_ix2
labels.columns = ['Cluster0', 'Cluster1', 'Cluster2']
test = test.join(labels)
df['listing_time'] = df['number_of_reviews'] / df['reviews_per_month']
df['listing_time'] = df['listing_time'].fillna(0)
df = df.drop(cols_to_drop, axis=1)
df['is_train'] = df['transit'].str.lower().str.contains('train')
df['is_bus'] = df['transit'].str.lower().str.contains('bus')
df['is_subway'] = df['transit'].str.lower().str.contains('subway')
df['is_cab'] = df['transit'].str.lower().str.contains('cab') | df['transit'].str.lower().str.contains('car') | df['transit'].str.lower().str.contains('uber') | df['transit'].str.lower().str.contains('taxi')
df['is_metro'] = df['transit'].str.lower().str.contains('metro')
df['is_walk'] = df['transit'].str.lower().str.contains('walk')
df['is_wifi'] = df['amenities'].str.lower().str.contains('wifi') | df['amenities'].str.lower().str.contains('internet')
df['is_kitchen'] = df['amenities'].str.lower().str.contains('kitchen')
df['is_heating'] = df['amenities'].str.lower().str.contains('heat')
df['is_ac'] = df['amenities'].str.lower().str.contains('air conditioning')
df['is_washer'] = df['amenities'].str.lower().str.contains('washer') | df['amenities'].str.lower().str.contains('dryer') | df['amenities'].str.lower().str.contains('dishwasher')
df['is_tv'] = df['amenities'].str.lower().str.contains('tv')
df['is_gym'] = df['amenities'].str.lower().str.contains('gym')
df['is_pets'] = df['amenities'].str.lower().str.contains('pet')
df['is_balcony'] = df['amenities'].str.lower().str.contains('balcony')
df['is_linen'] = df['amenities'].str.lower().str.contains('linen')
df['is_breakfast'] = df['amenities'].str.lower().str.contains('breakfast')
df['is_coffee'] = df['amenities'].str.lower().str.contains('coffee')
df['is_cooking'] = df['amenities'].str.lower().str.contains('cooking')
df['is_pool'] = df['amenities'].str.lower().str.contains('pool')
df['amenities'] = df['amenities'].str.strip('{').str.strip('}').str.split(',').apply(lambda x: len(x) if x[0] != '' else 0)
df['host_is_superhost'] = df['host_is_superhost'].replace({'f': False, 't': True})
df = df.drop(['transit'], axis=1)
for col in df[pd.Series(df.columns)[pd.Series(df.columns).str.contains('review')].to_list()].columns:
to_fill = pd.Series(df[col].sample(df[col].isnull().sum()))
to_fill.index = df[df[col].isnull()].index
df[col] = df[col].fillna(to_fill)
df[df.columns[df.columns.str.contains('^is')]] = df[df.columns[df.columns.str.contains('^is')]].fillna('False')
df = df.fillna(0)
df = df.reset_index(drop=True)
test['listing_time'] = test['number_of_reviews'] / test['reviews_per_month']
test['listing_time'] = test['listing_time'].fillna(0)
test = test.drop(cols_to_drop, axis=1)
test['is_train'] = test['transit'].str.lower().str.contains('train')
test['is_bus'] = test['transit'].str.lower().str.contains('bus')
test['is_subway'] = test['transit'].str.lower().str.contains('subway')
test['is_cab'] = test['transit'].str.lower().str.contains('cab') | test['transit'].str.lower().str.contains('car') | test['transit'].str.lower().str.contains('uber') | test['transit'].str.lower().str.contains('taxi')
test['is_metro'] = test['transit'].str.lower().str.contains('metro')
test['is_walk'] = test['transit'].str.lower().str.contains('walk')
test['is_wifi'] = test['amenities'].str.lower().str.contains('wifi') | test['amenities'].str.lower().str.contains('internet')
test['is_kitchen'] = test['amenities'].str.lower().str.contains('kitchen')
test['is_heating'] = test['amenities'].str.lower().str.contains('heat')
test['is_ac'] = test['amenities'].str.lower().str.contains('air conditioning')
test['is_washer'] = test['amenities'].str.lower().str.contains('washer') | test['amenities'].str.lower().str.contains('dryer') | test['amenities'].str.lower().str.contains('dishwasher')
test['is_tv'] = test['amenities'].str.lower().str.contains('tv')
test['is_gym'] = test['amenities'].str.lower().str.contains('gym')
test['is_pets'] = test['amenities'].str.lower().str.contains('pet')
test['is_balcony'] = test['amenities'].str.lower().str.contains('balcony')
test['is_linen'] = test['amenities'].str.lower().str.contains('linen')
test['is_breakfast'] = test['amenities'].str.lower().str.contains('breakfast')
test['is_coffee'] = test['amenities'].str.lower().str.contains('coffee')
test['is_cooking'] = test['amenities'].str.lower().str.contains('cooking')
test['is_pool'] = test['amenities'].str.lower().str.contains('pool')
test['amenities'] = test['amenities'].str.strip('{').str.strip('}').str.split(',').apply(lambda x: len(x) if x[0] != '' else 0)
test['host_is_superhost'] = test['host_is_superhost'].replace({'f': False, 't': True})
test = test.drop(['transit'], axis=1)
for col in test[pd.Series(test.columns)[pd.Series(test.columns).str.contains('review')].to_list()].columns:
to_fill = pd.Series(test[col].sample(test[col].isnull().sum()))
to_fill.index = test[test[col].isnull()].index
test[col] = test[col].fillna(to_fill)
test[test.columns[test.columns.str.contains('^is')]] = test[test.columns[test.columns.str.contains('^is')]].fillna('False')
test = test.fillna(0)
df['bedrooms'] = df['bedrooms'].apply(lambda x: str(x))
df['bathrooms'] = df['bathrooms'].apply(lambda x: str(x))
test['bedrooms'] = test['bedrooms'].apply(lambda x: str(x))
test['bathrooms'] = test['bathrooms'].apply(lambda x: str(x))
test = test.reset_index(drop=True)
return (df.replace({False: 0, True: 1, 'False': 0, 'True': 1, 'f': 0, 't': 1}), test.replace({False: 0, True: 1, 'False': 0, 'True': 1, 'f': 0, 't': 1}))
df, test = clean(df, test) | code |
34133142/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnbdm/train.csv')
test = pd.read_csv('/kaggle/input/airbnbdm/test.csv')
submission = pd.DataFrame()
submission['Id'] = test['id'].copy()
df.columns | code |
33111475/cell_13 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3)
plt.tight_layout()
Nigeria.groupby('Month_name')['No. of suspected cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of suspected deaths'].sum().nlargest(3) | code |
33111475/cell_9 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3) | code |
33111475/cell_6 | [
"text_plain_output_1.png"
] | from datetime import date
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
print(delta) | code |
33111475/cell_11 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3)
plt.subplot(1, 2, 1)
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3).plot(kind='bar', grid=True)
plt.title('Confirmed cases (3)')
plt.xlabel('Months')
plt.ylabel('No. of probable cases')
plt.subplot(1, 2, 2)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3).plot(kind='bar', grid=True, color='red')
plt.title('Confirmed deaths (3)')
plt.xlabel('Months')
plt.ylabel('No. of probable deaths')
plt.tight_layout()
plt.show() | code |
33111475/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33111475/cell_7 | [
"image_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
print('The date of Nigeria data is from', Nigeria.Dates.min(), 'to', Nigeria.Dates.max(), ',a total number of', delta)
print('The total number of confirmed cases in Nigeria is', Nigeria['No. of confirmed cases'].sum())
print('The total number of confirmed deaths in Nigeria is', Nigeria['No. of confirmed deaths'].sum())
print('The total number of suspected cases in Nigeria is', Nigeria['No. of suspected cases'].sum())
print('The total number of suspected deaths in Nigeria is', Nigeria['No. of suspected deaths'].sum())
print('The total number of probable cases in Nigeria is', Nigeria['No. of probable cases'].sum())
print('The total number of probable deaths in Nigeria is', Nigeria['No. of probable deaths'].sum()) | code |
33111475/cell_8 | [
"image_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum() | code |
33111475/cell_15 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3)
plt.tight_layout()
Nigeria.groupby('Month_name')['No. of suspected cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of suspected deaths'].sum().nlargest(3)
plt.subplot(1, 2, 1)
Nigeria.groupby('Month_name')['No. of probable cases'].sum().nlargest(3).plot(kind='bar', grid=True)
plt.title('Probable cases (3)')
plt.xlabel('Months')
plt.ylabel('No. of probable cases')
plt.subplot(1, 2, 2)
Nigeria.groupby('Month_name')['No. of probable deaths'].sum().nlargest(3).plot(kind='bar', grid=True, color='red')
plt.title('Probable deaths (3)')
plt.xlabel('Months')
plt.ylabel('No. of probable deaths')
plt.tight_layout()
plt.show() | code |
33111475/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
df.head() | code |
33111475/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3)
plt.tight_layout()
Nigeria.groupby('Month_name')['No. of suspected cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of suspected deaths'].sum().nlargest(3)
plt.subplot(1, 2, 1)
Nigeria.groupby('Month_name')['No. of suspected cases'].sum().nlargest(3).plot(kind='bar', grid=True)
plt.title('Suspected cases (3)')
plt.xlabel('Months')
plt.ylabel('No. of suspected cases')
plt.show() | code |
33111475/cell_10 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3) | code |
33111475/cell_12 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
Nigeria.groupby('Month_name')['No. of confirmed cases', 'No. of confirmed deaths'].sum()
Nigeria.groupby('Month_name')['No. of confirmed cases'].sum().nlargest(3)
Nigeria.groupby('Month_name')['No. of confirmed deaths'].sum().nlargest(3)
plt.tight_layout()
Nigeria.groupby('Month_name')['No. of suspected cases'].sum().nlargest(3) | code |
33111475/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_name'] = df.Dates.dt.month_name()
df['Day_name'] = df.Dates.dt.day_name()
df['Month'] = df.Dates.dt.month
df['Week'] = df.Dates.dt.week
df['Day_of_year'] = df.Dates.dt.dayofyear
Nigeria = df.loc[df.Country == 'Nigeria']
Nigeria.head() | code |
49115994/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno)
def check_duplicates(df, df_name):
print(f'{df_name}')
print(f'{df.Date.duplicated().value_counts()}')
print('')
check_duplicates(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
check_duplicates(df=Aquifer_Auser, df_name='Aquifer_Auser')
check_duplicates(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
check_duplicates(df=Lake_Bilancino, df_name='Lake_Bilancino')
check_duplicates(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
check_duplicates(df=Aquifer_Luco, df_name='Aquifer_Luco')
check_duplicates(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
check_duplicates(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
check_duplicates(df=River_Arno, df_name='River_Arno') | code |
49115994/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno)
def date_range_of_data(df, df_name):
print(f'The date range for ## {df_name} ## is from')
print(f"{df['Date'].min()} to {df['Date'].max()}")
print(f"which is a total of {(df['Date'].max() - df['Date'].min()).days} days")
print('')
date_range_of_data(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
date_range_of_data(df=Aquifer_Auser, df_name='Aquifer_Auser')
date_range_of_data(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
date_range_of_data(df=Lake_Bilancino, df_name='Lake_Bilancino')
date_range_of_data(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
date_range_of_data(df=Aquifer_Luco, df_name='Aquifer_Luco')
date_range_of_data(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
date_range_of_data(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
date_range_of_data(df=River_Arno, df_name='River_Arno') | code |
49115994/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno)
def check_duplicates(df, df_name):
pass
check_duplicates(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
check_duplicates(df=Aquifer_Auser, df_name='Aquifer_Auser')
check_duplicates(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
check_duplicates(df=Lake_Bilancino, df_name='Lake_Bilancino')
check_duplicates(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
check_duplicates(df=Aquifer_Luco, df_name='Aquifer_Luco')
check_duplicates(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
check_duplicates(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
check_duplicates(df=River_Arno, df_name='River_Arno')
def find_total_missing_days(df, df_name):
daily_date = pd.date_range(start=df.Date.min(), end=df.Date.max(), freq='D')
uniq_days = df.Date.nunique()
find_total_missing_days(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
find_total_missing_days(df=Aquifer_Auser, df_name='Aquifer_Auser')
find_total_missing_days(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
find_total_missing_days(df=Lake_Bilancino, df_name='Lake_Bilancino')
find_total_missing_days(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
find_total_missing_days(df=Aquifer_Luco, df_name='Aquifer_Luco')
find_total_missing_days(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
find_total_missing_days(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
find_total_missing_days(df=River_Arno, df_name='River_Arno')
def total_missing_days_pattern(df, df_name):
daily_date = pd.date_range(start=df.Date.min(), end=df.Date.max(), freq='D')
daily_data = pd.DataFrame({'Date': daily_date})
temp = df[['Date']].copy()
temp['missing'] = 0
final = daily_data.merge(temp, on=['Date'], how='left')
final.fillna(1, inplace=True)
total_missing_days_pattern(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
total_missing_days_pattern(df=Aquifer_Auser, df_name='Aquifer_Auser')
total_missing_days_pattern(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
total_missing_days_pattern(df=Lake_Bilancino, df_name='Lake_Bilancino')
total_missing_days_pattern(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
total_missing_days_pattern(df=Aquifer_Luco, df_name='Aquifer_Luco')
total_missing_days_pattern(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
total_missing_days_pattern(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
total_missing_days_pattern(df=River_Arno, df_name='River_Arno')
def Null_analysis(df, title):
temp = df.isnull().sum()
plt.figure(figsize=(15, 5))
g = sns.barplot(temp.index, temp.values)
plt.xticks(rotation=90)
plt.ylim(0, temp.values.max() + 1000)
plt.title(title)
for p in g.patches:
g.annotate('{:.0f}\n{:.2f}%'.format(p.get_height(), p.get_height() / df.shape[0]), (p.get_x() + 0.4, p.get_height() + 10), ha='center', va='bottom', color='black')
plt.show()
Null_analysis(df=Aquifer_Doganella, title='Aquifer_Doganella')
Null_analysis(df=Aquifer_Auser, title='Aquifer_Auser')
Null_analysis(df=Water_Spring_Amiata, title='Water_Spring_Amiata')
Null_analysis(df=Lake_Bilancino, title='Lake_Bilancino')
Null_analysis(df=Water_Spring_Madonna_di_Canneto, title='Water_Spring_Madonna_di_Canneto')
Null_analysis(df=Aquifer_Luco, title='Aquifer_Luco')
Null_analysis(df=Aquifer_Petrignano, title='Aquifer_Petrignano')
Null_analysis(df=Water_Spring_Lupa, title='Water_Spring_Lupa')
Null_analysis(df=River_Arno, title='River_Arno') | code |
49115994/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno)
def check_duplicates(df, df_name):
pass
check_duplicates(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
check_duplicates(df=Aquifer_Auser, df_name='Aquifer_Auser')
check_duplicates(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
check_duplicates(df=Lake_Bilancino, df_name='Lake_Bilancino')
check_duplicates(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
check_duplicates(df=Aquifer_Luco, df_name='Aquifer_Luco')
check_duplicates(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
check_duplicates(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
check_duplicates(df=River_Arno, df_name='River_Arno')
def find_total_missing_days(df, df_name):
daily_date = pd.date_range(start=df.Date.min(), end=df.Date.max(), freq='D')
uniq_days = df.Date.nunique()
find_total_missing_days(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
find_total_missing_days(df=Aquifer_Auser, df_name='Aquifer_Auser')
find_total_missing_days(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
find_total_missing_days(df=Lake_Bilancino, df_name='Lake_Bilancino')
find_total_missing_days(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
find_total_missing_days(df=Aquifer_Luco, df_name='Aquifer_Luco')
find_total_missing_days(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
find_total_missing_days(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
find_total_missing_days(df=River_Arno, df_name='River_Arno')
def total_missing_days_pattern(df, df_name):
daily_date = pd.date_range(start=df.Date.min(), end=df.Date.max(), freq='D')
daily_data = pd.DataFrame({'Date': daily_date})
temp = df[['Date']].copy()
temp['missing'] = 0
final = daily_data.merge(temp, on=['Date'], how='left')
final.fillna(1, inplace=True)
plt.figure(figsize=(20, 5))
sns.scatterplot(final.Date, final.missing, hue=final.missing)
plt.title(df_name)
total_missing_days_pattern(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
total_missing_days_pattern(df=Aquifer_Auser, df_name='Aquifer_Auser')
total_missing_days_pattern(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
total_missing_days_pattern(df=Lake_Bilancino, df_name='Lake_Bilancino')
total_missing_days_pattern(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
total_missing_days_pattern(df=Aquifer_Luco, df_name='Aquifer_Luco')
total_missing_days_pattern(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
total_missing_days_pattern(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
total_missing_days_pattern(df=River_Arno, df_name='River_Arno') | code |
49115994/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno)
def check_duplicates(df, df_name):
pass
check_duplicates(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
check_duplicates(df=Aquifer_Auser, df_name='Aquifer_Auser')
check_duplicates(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
check_duplicates(df=Lake_Bilancino, df_name='Lake_Bilancino')
check_duplicates(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
check_duplicates(df=Aquifer_Luco, df_name='Aquifer_Luco')
check_duplicates(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
check_duplicates(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
check_duplicates(df=River_Arno, df_name='River_Arno')
def find_total_missing_days(df, df_name):
daily_date = pd.date_range(start=df.Date.min(), end=df.Date.max(), freq='D')
uniq_days = df.Date.nunique()
print(f'#### {df_name}')
print(f'Total unique days we have data: {uniq_days}')
print(f'Total days missing: {len(daily_date) - uniq_days}')
print('')
find_total_missing_days(df=Aquifer_Doganella, df_name='Aquifer_Doganella')
find_total_missing_days(df=Aquifer_Auser, df_name='Aquifer_Auser')
find_total_missing_days(df=Water_Spring_Amiata, df_name='Water_Spring_Amiata')
find_total_missing_days(df=Lake_Bilancino, df_name='Lake_Bilancino')
find_total_missing_days(df=Water_Spring_Madonna_di_Canneto, df_name='Water_Spring_Madonna_di_Canneto')
find_total_missing_days(df=Aquifer_Luco, df_name='Aquifer_Luco')
find_total_missing_days(df=Aquifer_Petrignano, df_name='Aquifer_Petrignano')
find_total_missing_days(df=Water_Spring_Lupa, df_name='Water_Spring_Lupa')
find_total_missing_days(df=River_Arno, df_name='River_Arno') | code |
49115994/cell_5 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read_csv('/kaggle/input/acea-water-prediction/Lake_Bilancino.csv')
Water_Spring_Madonna_di_Canneto = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Madonna_di_Canneto.csv')
Aquifer_Luco = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Luco.csv')
Aquifer_Petrignano = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Petrignano.csv')
Water_Spring_Lupa = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Lupa.csv')
River_Arno = pd.read_csv('/kaggle/input/acea-water-prediction/River_Arno.csv')
Aquifer_Doganella['Date'] = pd.to_datetime(Aquifer_Doganella['Date'])
Aquifer_Auser['Date'] = pd.to_datetime(Aquifer_Auser['Date'])
Water_Spring_Amiata['Date'] = pd.to_datetime(Water_Spring_Amiata['Date'])
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'])
Water_Spring_Madonna_di_Canneto['Date'] = pd.to_datetime(Water_Spring_Madonna_di_Canneto['Date'])
Aquifer_Luco['Date'] = pd.to_datetime(Aquifer_Luco['Date'])
Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'])
Water_Spring_Lupa['Date'] = pd.to_datetime(Water_Spring_Lupa['Date'])
River_Arno['Date'] = pd.to_datetime(River_Arno['Date'])
def get_datefeatures(df):
df['month'] = df.Date.dt.month
df['day'] = df.Date.dt.day
df['week'] = df.Date.dt.week
df['year'] = df.Date.dt.year
return df
Aquifer_Doganella = get_datefeatures(df=Aquifer_Doganella)
Aquifer_Auser = get_datefeatures(df=Aquifer_Auser)
Water_Spring_Amiata = get_datefeatures(df=Water_Spring_Amiata)
Lake_Bilancino = get_datefeatures(df=Lake_Bilancino)
Water_Spring_Madonna_di_Canneto = get_datefeatures(df=Water_Spring_Madonna_di_Canneto)
Aquifer_Luco = get_datefeatures(df=Aquifer_Luco)
Aquifer_Petrignano = get_datefeatures(df=Aquifer_Petrignano)
Water_Spring_Lupa = get_datefeatures(df=Water_Spring_Lupa)
River_Arno = Water_Spring_Lupa = get_datefeatures(df=River_Arno) | code |
89130056/cell_13 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
file_path = '../input/painter-by-numbers/'
archive = zipfile.ZipFile(file_path + 'replacements_for_corrupted_files.zip', 'r')
corrupted_ids = set()
for item in archive.namelist():
ID = re.sub('[^0-9]', '', item)
if ID != '':
corrupted_ids.add(ID)
drop_idx = []
for index, row in df.iterrows():
id_check = re.sub('[^0-9]', '', row['new_filename'])
if id_check in corrupted_ids:
drop_idx.append(index)
df = df.drop(drop_idx)
painter_dict = {'Kandinsky': '', 'Dali': '', 'Picasso': '', 'Delacroix': '', 'Rembrandt': '', 'Gogh': '', 'Kuniyoshi': '', 'Dore': '', 'Steinlen': '', 'Saryan': '', 'Goya': '', 'Lautrec': '', 'Modigliani': '', 'Beksinski': '', 'Pissarro': '', 'Kirchner': '', 'Renoir': '', 'Piranesi': '', 'Degas': '', 'Chagall': ''}
paintings_dict = painter_dict.copy()
for artist in painter_dict:
for painter in df['artist']:
if artist in painter:
painter_dict[artist] = painter
paintings = df[df['artist'] == painter].shape[0]
paintings_dict[artist] = paintings
break
sample_size = min(paintings_dict.values())
min_a = list(paintings_dict.keys())[list(paintings_dict.values()).index(sample_size)]
active_df = pd.DataFrame({})
for artist in painter_dict.values():
tr_df = df[df['artist'] == artist].sort_values(by=['in_train', 'size_bytes'], ascending=[False, True])
active_df = pd.concat([active_df, tr_df.iloc[:sample_size]])
artists = list(painter_dict.values())
LabEnc = preprocessing.LabelEncoder()
LabEnc.fit(artists) | code |
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