path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
32071924/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum()
all_data = pd.concat([train_df, test_df], axis=0, sort=False)
all_data['Province_State'].fillna('None', inplace=True)
all_data['ConfirmedCases'].fillna(0, inplace=True)
all_data['Fatalities'].fillna(0, inplace=True)
all_data['Id'].fillna(-1, inplace=True)
all_data['ForecastId'].fillna(-1, inplace=True)
all_data.head() | code |
32071924/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
print(test_df.shape)
test_df.head() | code |
32071924/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
test_df.isna().sum() | code |
32071924/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum()
all_data = pd.concat([train_df, test_df], axis=0, sort=False)
all_data['Province_State'].fillna('None', inplace=True)
all_data['ConfirmedCases'].fillna(0, inplace=True)
all_data['Fatalities'].fillna(0, inplace=True)
all_data['Id'].fillna(-1, inplace=True)
all_data['ForecastId'].fillna(-1, inplace=True)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
all_data['Date'] = pd.to_datetime(all_data['Date'])
all_data['Day_num'] = le.fit_transform(all_data.Date)
all_data['Day'] = all_data['Date'].dt.day
all_data['Month'] = all_data['Date'].dt.month
all_data['Year'] = all_data['Date'].dt.year
all_data.head() | code |
32071924/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
train_df['Province_State'].unique() | code |
32071924/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum()
all_data = pd.concat([train_df, test_df], axis=0, sort=False)
all_data['Province_State'].fillna('None', inplace=True)
all_data['ConfirmedCases'].fillna(0, inplace=True)
all_data['Fatalities'].fillna(0, inplace=True)
all_data['Id'].fillna(-1, inplace=True)
all_data['ForecastId'].fillna(-1, inplace=True)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
all_data['Date'] = pd.to_datetime(all_data['Date'])
all_data['Day_num'] = le.fit_transform(all_data.Date)
all_data['Day'] = all_data['Date'].dt.day
all_data['Month'] = all_data['Date'].dt.month
all_data['Year'] = all_data['Date'].dt.year
train = all_data[all_data['ForecastId'] == -1.0]
test = all_data[all_data['ForecastId'] != -1.0]
temp = train.groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
temp = temp.melt(id_vars='Date', value_vars=['ConfirmedCases', 'Fatalities'], var_name='case', value_name='count')
fig = px.line(temp, x='Date', y='count', color='case', title='Total cases over the Date ', color_discrete_sequence=['cyan', 'red'])
country_max = train.groupby(['Date', 'Country_Region'])['ConfirmedCases', 'Fatalities'].max().reset_index().sort_values(by='ConfirmedCases', ascending=False).groupby('Country_Region').max().reset_index().sort_values(by='ConfirmedCases', ascending=False)
country_max[:20].style.background_gradient(cmap='viridis_r') | code |
32071924/cell_16 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum()
all_data = pd.concat([train_df, test_df], axis=0, sort=False)
all_data['Province_State'].fillna('None', inplace=True)
all_data['ConfirmedCases'].fillna(0, inplace=True)
all_data['Fatalities'].fillna(0, inplace=True)
all_data['Id'].fillna(-1, inplace=True)
all_data['ForecastId'].fillna(-1, inplace=True)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
all_data['Date'] = pd.to_datetime(all_data['Date'])
all_data['Day_num'] = le.fit_transform(all_data.Date)
all_data['Day'] = all_data['Date'].dt.day
all_data['Month'] = all_data['Date'].dt.month
all_data['Year'] = all_data['Date'].dt.year
train = all_data[all_data['ForecastId'] == -1.0]
test = all_data[all_data['ForecastId'] != -1.0]
temp = train.groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
temp = temp.melt(id_vars='Date', value_vars=['ConfirmedCases', 'Fatalities'], var_name='case', value_name='count')
fig = px.line(temp, x='Date', y='count', color='case', title='Total cases over the Date ', color_discrete_sequence=['cyan', 'red'])
country_max = train.groupby(['Date', 'Country_Region'])['ConfirmedCases', 'Fatalities'].max().reset_index().sort_values(by='ConfirmedCases', ascending=False).groupby('Country_Region').max().reset_index().sort_values(by='ConfirmedCases', ascending=False)
country_max[:20].style.background_gradient(cmap='viridis_r')
Top_country = train.groupby('Country_Region')['ConfirmedCases', 'Fatalities'].max().reset_index().sort_values(by='ConfirmedCases', ascending=False).head(15)
fig_c = px.bar(Top_country.sort_values('ConfirmedCases'), x='ConfirmedCases', y='Country_Region', text='ConfirmedCases', orientation='h', color_discrete_sequence=['cyan'])
fig_d = px.bar(Top_country.sort_values('Fatalities'), x='Fatalities', y='Country_Region', text='Fatalities', orientation='h', color_discrete_sequence=['red'])
fig = make_subplots(rows=1, cols=2, shared_xaxes=False, horizontal_spacing=0.14, vertical_spacing=0.08, subplot_titles=('Confirmedcases', 'Fatalities'))
fig.add_trace(fig_c['data'][0], row=1, col=1)
fig.add_trace(fig_d['data'][0], row=1, col=2) | code |
32071924/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
print(train_df.shape)
train_df.tail() | code |
32071924/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum()
all_data = pd.concat([train_df, test_df], axis=0, sort=False)
all_data['Province_State'].fillna('None', inplace=True)
all_data['ConfirmedCases'].fillna(0, inplace=True)
all_data['Fatalities'].fillna(0, inplace=True)
all_data['Id'].fillna(-1, inplace=True)
all_data['ForecastId'].fillna(-1, inplace=True)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
all_data['Date'] = pd.to_datetime(all_data['Date'])
all_data['Day_num'] = le.fit_transform(all_data.Date)
all_data['Day'] = all_data['Date'].dt.day
all_data['Month'] = all_data['Date'].dt.month
all_data['Year'] = all_data['Date'].dt.year
train = all_data[all_data['ForecastId'] == -1.0]
test = all_data[all_data['ForecastId'] != -1.0]
temp = train.groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
temp = temp.melt(id_vars='Date', value_vars=['ConfirmedCases', 'Fatalities'], var_name='case', value_name='count')
fig = px.line(temp, x='Date', y='count', color='case', title='Total cases over the Date ', color_discrete_sequence=['cyan', 'red'])
fig.show() | code |
32071924/cell_5 | [
"text_html_output_2.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum() | code |
88078973/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine | code |
88078973/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.head() | code |
88078973/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sns.pairplot(wine_data2) | code |
88078973/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
wine_data2['rating'].describe() | code |
88078973/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes | code |
88078973/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sample = wine_data2.sample(frac=0.01)
sns.set_style('whitegrid')
sample = wine_data2.sample(frac=0.01)
sns.boxplot(x='category', y='rating', data=wine_data2) | code |
88078973/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sample = wine_data2.sample(frac=0.01)
sns.set_style('whitegrid')
sample = wine_data2.sample(frac=0.01)
sns.set(style='whitegrid')
plt.figure(figsize=(12, 10))
boxplot = sns.boxplot(x='category', y='alcohol_num', data=wine_data2)
boxplot.set_ylim(0, 100) | code |
88078973/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
wine_data2 | code |
88078973/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88078973/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
sns.displot(wine_data2.loc[lambda _wine_data: _wine_data['price_num'] < 125]['price_num'], bins=30)
plt.show() | code |
88078973/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.describe() | code |
88078973/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data['alcohol'] | code |
88078973/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data | code |
88078973/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sample = wine_data2.sample(frac=0.01)
sns.regplot(x='alcohol_num', y='rating', data=sample.loc[lambda _df: _df['alcohol_num'] < 20]) | code |
88078973/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sns.displot(wine_data2, x='category', height=6) | code |
88078973/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum() | code |
88078973/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine | code |
88078973/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum() | code |
88078973/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine | code |
88078973/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data = wine_data.assign(alcohol_num=lambda row: row['alcohol'].replace('%', '', regex=True).astype('float'))
wine_data1 = wine_data.dropna(subset=['price'])
price_nums = []
for index, row in wine_data1.iterrows():
if row['price'].replace('$', '').isdecimal():
price_nums.append(float(row['price'].replace('$', '')))
else:
price_nums.append(np.nan)
wine_data2 = wine_data1.copy()
wine_data2['price_num'] = price_nums
wine_data2 = wine_data2.dropna(subset=['price_num'])
Top_rated_wine = wine_data2.sort_values('rating', ascending=False).head(10)
Top_rated_wine
top_10_expensive_wine = wine_data2.sort_values('price', ascending=False).head(10)
top_10_expensive_wine
Top_10_cheapest_wine = wine_data2.sort_values('price', ascending=True).head(10)
Top_10_cheapest_wine
sample = wine_data2.sample(frac=0.01)
sns.set_style('whitegrid')
sample = wine_data2.sample(frac=0.01)
sns.regplot(x='price_num', y='rating', data=sample.loc[lambda _wine_data2: _wine_data2['price_num'] < 220]) | code |
50222837/cell_13 | [
"image_output_1.png"
] | """import xgboost
xgBoost = xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, booster='gbtree')
xgBoost.fit(X_train, Y_train)
print("train score", xgBoost.score(X_train, Y_train))
print("test score", xgBoost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(xgBoost, X, Y, cv=3).mean())""" | code |
50222837/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent_nan > 0].sort_values()
return percent_nan
nanColums = NanColums(df)
plt.xticks(rotation=90)
df[['Electrical', 'MasVnrType']] = df[['Electrical', 'MasVnrType']].fillna('None')
df[['MasVnrArea']] = df[['MasVnrArea']].fillna(0)
str_cols = ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond', 'BsmtFinType1', 'BsmtFinType2', 'BsmtExposure']
df[str_cols] = df[str_cols].fillna('None')
df['GarageYrBlt'] = df['GarageYrBlt'].fillna(0)
df.drop(['Fence', 'Alley', 'MiscFeature', 'PoolQC'], axis=1, inplace=True)
df['FireplaceQu'] = df['FireplaceQu'].fillna('None')
df['LotFrontage'] = df.groupby('Neighborhood')['LotFrontage'].transform(lambda val: val.fillna(val.mean()))
'del df["Alley"] #91 delete due to almost all ia NaN\ndel df["PoolQC"] #7\ndel df["MiscFeature"] #54\n\n\ndf["MasVnrType"].fillna(value="0", inplace=True) # 1452\ndf["MasVnrArea"].fillna(value=0.0, inplace=True) # 1452\ndf["BsmtQual"].fillna(value="0", inplace=True) # 1423\ndf["BsmtCond"].fillna(value="0", inplace=True) # 1423\ndf["BsmtExposure"].fillna(value="0", inplace=True) # 1422\ndf["BsmtFinType1"].fillna(value="0", inplace=True) # 1423\ndf["BsmtFinType2"].fillna(value="0", inplace=True) # 1422\ndf["FireplaceQu"].fillna(value="0", inplace=True) #770\n\ndf["Electrical"].fillna(value="0", inplace=True) #1459\ndf["GarageType"].fillna(value="0", inplace=True) #1379 осутствие логично закодировать нулем\ndf["GarageYrBlt"].fillna(value=0.0, inplace=True) # 1379\ndf["GarageFinish"].fillna(value="0", inplace=True) #1379\ndf["GarageQual"].fillna(value="0", inplace=True) #1379\ndf["GarageCond"].fillna(value="0", inplace=True) #1379\ndf["Fence"].fillna(value="0", inplace=True) #281\n\ndf[\'LotFrontage\'] = df.groupby(\'Neighborhood\')[\'LotFrontage\'].transform(lambda val: val.fillna(val.mean()))\n\n#categ data\n#df = pd.get_dummies(df)'
sns.heatmap(df.corr(), xticklabels=True, yticklabels=True)
plt.show() | code |
50222837/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('../input/house-prices-advanced-regression-techniques/train.csv')
df.info() | code |
50222837/cell_20 | [
"text_plain_output_1.png"
] | """import lightgbm
params = [
{
# 'regressor__regressor': [lightgbm.LGBMRegressor()],
'regressor__regressor__boosting_type': ['gbdt'],
'regressor__regressor__n_estimators': [100],
'regressor__regressor__max_depth': [20],
'regressor__regressor__learning_rate' : [0.1],
'regressor__regressor__num_leaves' : [31],
},
]
gsc = GridSearchCV(
estimator=lightgbm.LGBMRegressor(),
param_grid=params,
cv=320,
scoring='r2',
verbose=0,
n_jobs=-1)
grid_result = gsc.fit(X_train, Y_train)
print('Best params:', grid_result.best_params_)
print('Best score:', grid_result.best_score_)
lgbreg = lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, learning_rate=0.1, n_estimators=100, max_depth = 20)
lgbreg.fit(X_train, Y_train)
print("train score", lgbreg.score(X_train, Y_train))
print("test score", lgbreg.score(X_test, Y_test))
#print("crossVal score", cross_val_score(lgbreg, X, Y, cv=3).mean())""" | code |
50222837/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent_nan > 0].sort_values()
return percent_nan
nanColums = NanColums(df)
plt.xticks(rotation=90)
df[['Electrical', 'MasVnrType']] = df[['Electrical', 'MasVnrType']].fillna('None')
df[['MasVnrArea']] = df[['MasVnrArea']].fillna(0)
str_cols = ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond', 'BsmtFinType1', 'BsmtFinType2', 'BsmtExposure']
df[str_cols] = df[str_cols].fillna('None')
df['GarageYrBlt'] = df['GarageYrBlt'].fillna(0)
df.drop(['Fence', 'Alley', 'MiscFeature', 'PoolQC'], axis=1, inplace=True)
df['FireplaceQu'] = df['FireplaceQu'].fillna('None')
df['LotFrontage'] = df.groupby('Neighborhood')['LotFrontage'].transform(lambda val: val.fillna(val.mean()))
'del df["Alley"] #91 delete due to almost all ia NaN\ndel df["PoolQC"] #7\ndel df["MiscFeature"] #54\ndf["MasVnrType"].fillna(value="0", inplace=True) # 1452\ndf["MasVnrArea"].fillna(value=0.0, inplace=True) # 1452\ndf["BsmtQual"].fillna(value="0", inplace=True) # 1423\ndf["BsmtCond"].fillna(value="0", inplace=True) # 1423\ndf["BsmtExposure"].fillna(value="0", inplace=True) # 1422\ndf["BsmtFinType1"].fillna(value="0", inplace=True) # 1423\ndf["BsmtFinType2"].fillna(value="0", inplace=True) # 1422\ndf["FireplaceQu"].fillna(value="0", inplace=True) #770\ndf["Electrical"].fillna(value="0", inplace=True) #1459\ndf["GarageType"].fillna(value="0", inplace=True) #1379 осутствие логично закодировать нулем\ndf["GarageYrBlt"].fillna(value=0.0, inplace=True) # 1379\ndf["GarageFinish"].fillna(value="0", inplace=True) #1379\ndf["GarageQual"].fillna(value="0", inplace=True) #1379\ndf["GarageCond"].fillna(value="0", inplace=True) #1379\ndf["Fence"].fillna(value="0", inplace=True) #281\ndf[\'LotFrontage\'] = df.groupby(\'Neighborhood\')[\'LotFrontage\'].transform(lambda val: val.fillna(val.mean()))\n#categ data\n#df = pd.get_dummies(df)' | code |
50222837/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.transform(X_test)
from sklearn.linear_model import ElasticNet
basic_elastic_model = ElasticNet(max_iter=1000000)
param_grid = {'alpha': [100, 120, 130, 140, 200], 'l1_ratio': [0.1, 0.7, 0.99, 1]}
from sklearn.model_selection import GridSearchCV
grid_model = GridSearchCV(estimator=basic_elastic_model, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5, verbose=1)
grid_model.fit(scaled_X_train, y_train) | code |
50222837/cell_11 | [
"image_output_1.png"
] | """import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
print("train score", sklearn_boost.score(X_train, Y_train))
print("test score", sklearn_boost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(sklearn_boost, X, Y, cv=3).mean())""" | code |
50222837/cell_19 | [
"text_plain_output_1.png"
] | """import xgboost
params = [
{
'learning_rate' : [0.2],
'n_estimators': [250],
'max_depth': [3],
},
]
gsc = GridSearchCV(
estimator=xgboost.XGBRegressor(),
param_grid=params,
cv=3,
scoring='r2',
verbose=0,
n_jobs=-1)
grid_result = gsc.fit(X_train, Y_train)
print('Best params:', grid_result.best_params_)
print('Best score:', grid_result.best_score_)
xgBoost = xgboost.XGBRegressor(max_depth=3, learning_rate=0.2, n_estimators=250, booster='gbtree')
xgBoost.fit(X_train, Y_train)
print("train score", xgBoost.score(X_train, Y_train))
print("test score", xgBoost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(xgBoost, X, Y, cv=3).mean())""" | code |
50222837/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 |
50222837/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent_nan > 0].sort_values()
return percent_nan
nanColums = NanColums(df)
plt.xticks(rotation=90)
df[['Electrical', 'MasVnrType']] = df[['Electrical', 'MasVnrType']].fillna('None')
df[['MasVnrArea']] = df[['MasVnrArea']].fillna(0)
str_cols = ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond', 'BsmtFinType1', 'BsmtFinType2', 'BsmtExposure']
df[str_cols] = df[str_cols].fillna('None')
df['GarageYrBlt'] = df['GarageYrBlt'].fillna(0)
df.drop(['Fence', 'Alley', 'MiscFeature', 'PoolQC'], axis=1, inplace=True)
df['FireplaceQu'] = df['FireplaceQu'].fillna('None')
df['LotFrontage'] = df.groupby('Neighborhood')['LotFrontage'].transform(lambda val: val.fillna(val.mean()))
'del df["Alley"] #91 delete due to almost all ia NaN\ndel df["PoolQC"] #7\ndel df["MiscFeature"] #54\n\n\ndf["MasVnrType"].fillna(value="0", inplace=True) # 1452\ndf["MasVnrArea"].fillna(value=0.0, inplace=True) # 1452\ndf["BsmtQual"].fillna(value="0", inplace=True) # 1423\ndf["BsmtCond"].fillna(value="0", inplace=True) # 1423\ndf["BsmtExposure"].fillna(value="0", inplace=True) # 1422\ndf["BsmtFinType1"].fillna(value="0", inplace=True) # 1423\ndf["BsmtFinType2"].fillna(value="0", inplace=True) # 1422\ndf["FireplaceQu"].fillna(value="0", inplace=True) #770\n\ndf["Electrical"].fillna(value="0", inplace=True) #1459\ndf["GarageType"].fillna(value="0", inplace=True) #1379 осутствие логично закодировать нулем\ndf["GarageYrBlt"].fillna(value=0.0, inplace=True) # 1379\ndf["GarageFinish"].fillna(value="0", inplace=True) #1379\ndf["GarageQual"].fillna(value="0", inplace=True) #1379\ndf["GarageCond"].fillna(value="0", inplace=True) #1379\ndf["Fence"].fillna(value="0", inplace=True) #281\n\ndf[\'LotFrontage\'] = df.groupby(\'Neighborhood\')[\'LotFrontage\'].transform(lambda val: val.fillna(val.mean()))\n\n#categ data\n#df = pd.get_dummies(df)'
plt.figure(figsize=(10, 8))
sns.distplot(df['SalePrice'])
plt.show() | code |
50222837/cell_18 | [
"text_plain_output_1.png"
] | """from catboost import CatBoost
params = {
'depth': [7],
'learning_rate' : [0.15],
'l2_leaf_reg': [15,20, 25],
'iterations': [300],
'verbose' : [False], #shut up!!!
}
gsc = GridSearchCV(
estimator=catboost.CatBoostRegressor(),
param_grid=params,
cv=3,
scoring='r2',
verbose=0,
n_jobs=-1)
grid_result = gsc.fit(X_train, Y_train)
print('Best params:', grid_result.best_params_)
print('Best score:', grid_result.best_score_)
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False, learning_rate = 0.15, l2_leaf_reg = 20, iterations = 300)
cboost.fit(X_train, Y_train)
print("train score", cboost.score(X_train, Y_train))
print("test score", cboost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(cboost, X, Y, cv=3).mean())""" | code |
50222837/cell_15 | [
"text_plain_output_1.png"
] | """#стакнем-ка ридж регерссию и метод опорных векторов
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
import warnings
warnings.filterwarnings('ignore') #нужна линеризация данных, а мне лень
estimators = [('lr', RidgeCV()), ('svr', LinearSVR(random_state=42, max_iter = 1000))]
regStack = StackingRegressor(estimators=estimators, final_estimator=RandomForestRegressor(n_estimators=10, random_state=42))
regStack.fit(X_train, Y_train)
print("train score", regStack.score(X_train, Y_train))
print("test score", regStack.score(X_test, Y_test))
#print("crossVal score", cross_val_score(regStack, X, Y, cv=3).mean())""" | code |
50222837/cell_16 | [
"text_plain_output_1.png"
] | """#среднее по рандомным деревьям показывает неплохой результат
from sklearn.ensemble import RandomForestRegressor
Begging = RandomForestRegressor(max_depth=30, n_estimators=300)
Begging.fit(X_train, Y_train)
print("train score", Begging.score(X_train, Y_train))
print("test score", Begging.score(X_test, Y_test))
#print("crossVal score", cross_val_score(Begging, X, Y, cv=3).mean())""" | code |
50222837/cell_17 | [
"text_plain_output_1.png"
] | """import sklearn
params = {
'learning_rate': [0.05],
'n_estimators' : [200],
'max_depth' : [6]
}
gsc = GridSearchCV(
estimator=ensemble.GradientBoostingRegressor(),
param_grid=params,
cv=3)
grid_result = gsc.fit(X_train, Y_train)
print('Best params:', grid_result.best_params_)
print('Best score:', grid_result.best_score_)
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=200, max_depth=6)
sklearn_boost.fit(X_train, Y_train)
print("train score", sklearn_boost.score(X_train, Y_train))
print("test score", sklearn_boost.score(X_test, Y_test))""" | code |
50222837/cell_14 | [
"image_output_1.png"
] | """import lightgbm
lgbreg = lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, learning_rate=0.1, n_estimators=100)
lgbreg.fit(X_train, Y_train)
print("train score", lgbreg.score(X_train, Y_train))
print("test score", lgbreg.score(X_test, Y_test))
#print("crossVal score", cross_val_score(lgbreg, X, Y, cv=3).mean())""" | code |
50222837/cell_10 | [
"text_plain_output_1.png"
] | """Y = df["SalePrice"] #value for prediction
X = df.drop("SalePrice", axis=1) #data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=98987)""" | code |
50222837/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.transform(X_test)
from sklearn.linear_model import ElasticNet
basic_elastic_model = ElasticNet(max_iter=1000000)
param_grid = {'alpha': [100, 120, 130, 140, 200], 'l1_ratio': [0.1, 0.7, 0.99, 1]}
from sklearn.model_selection import GridSearchCV
grid_model = GridSearchCV(estimator=basic_elastic_model, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5, verbose=1)
grid_model.fit(scaled_X_train, y_train)
grid_model.best_params_ | code |
50222837/cell_12 | [
"text_plain_output_1.png"
] | """import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
cboost.fit(X_train, Y_train)
print("train score", cboost.score(X_train, Y_train))
print("test score", cboost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(cboost, X, Y, cv=3).mean())""" | code |
50222837/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent_nan > 0].sort_values()
return percent_nan
nanColums = NanColums(df)
sns.barplot(x=nanColums.index, y=nanColums)
plt.xticks(rotation=90) | code |
73075118/cell_9 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model = LinearDiscriminantAnalysis(n_components=2)
lda_model.fit(X, y)
reduced_X = lda_model.transform(X).T
knnmodel = KNeighborsClassifier(n_neighbors=3)
knnmodel.fit(reduced_X.T, y) | code |
73075118/cell_19 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model = LinearDiscriminantAnalysis(n_components=2)
lda_model.fit(X, y)
reduced_X = lda_model.transform(X).T
fig = px.scatter(
glass,
x=reduced_X[0],
y=reduced_X[1],
color="Type",
hover_data=['Type'],
color_continuous_scale='portland')
fig.show()
knnmodel = KNeighborsClassifier(n_neighbors=3)
knnmodel.fit(reduced_X.T, y)
fig = px.scatter(glass, x="Mg", y="Fe", color='Type', color_continuous_scale='portland')
fig.show()
fig = make_subplots(rows=1, cols=1)
fig.add_trace(go.Contour(
x=X['Mg'],
y=X['Fe'],
z=lda_model.predict(X),
showscale=False,
opacity=0.40,
colorscale='portland'
), row=1, col=1)
fig.add_trace(go.Scatter(
x=X['Mg'],
y=X['Fe'],
text=y,
mode='markers',
marker_symbol=y,
marker=dict(color=y, colorscale='portland')
), row=1, col=1)
fig.update_layout(showlegend=False)
fig.show()
fig = make_subplots(rows=1, cols=1)
fig.add_trace(go.Contour(x=reduced_X[0], y=reduced_X[1], z=lda_model.predict(X), showscale=False, opacity=0.4, colorscale='portland'), row=1, col=1)
fig.add_trace(go.Scatter(x=reduced_X[0], y=reduced_X[1], text=y, mode='markers', marker_symbol=y, marker=dict(color=y, colorscale='portland')), row=1, col=1)
fig.show() | code |
73075118/cell_7 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model = LinearDiscriminantAnalysis(n_components=2)
lda_model.fit(X, y)
reduced_X = lda_model.transform(X).T
fig = px.scatter(glass, x=reduced_X[0], y=reduced_X[1], color='Type', hover_data=['Type'], color_continuous_scale='portland')
fig.show() | code |
73075118/cell_16 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model = LinearDiscriminantAnalysis(n_components=2)
lda_model.fit(X, y)
reduced_X = lda_model.transform(X).T
fig = px.scatter(
glass,
x=reduced_X[0],
y=reduced_X[1],
color="Type",
hover_data=['Type'],
color_continuous_scale='portland')
fig.show()
fig = px.scatter(glass, x='Mg', y='Fe', color='Type', color_continuous_scale='portland')
fig.show() | code |
73075118/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
glass | code |
73075118/cell_17 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model = LinearDiscriminantAnalysis(n_components=2)
lda_model.fit(X, y)
reduced_X = lda_model.transform(X).T
fig = px.scatter(
glass,
x=reduced_X[0],
y=reduced_X[1],
color="Type",
hover_data=['Type'],
color_continuous_scale='portland')
fig.show()
fig = px.scatter(glass, x="Mg", y="Fe", color='Type', color_continuous_scale='portland')
fig.show()
fig = make_subplots(rows=1, cols=1)
fig.add_trace(go.Contour(x=X['Mg'], y=X['Fe'], z=lda_model.predict(X), showscale=False, opacity=0.4, colorscale='portland'), row=1, col=1)
fig.add_trace(go.Scatter(x=X['Mg'], y=X['Fe'], text=y, mode='markers', marker_symbol=y, marker=dict(color=y, colorscale='portland')), row=1, col=1)
fig.update_layout(showlegend=False)
fig.show() | code |
33108543/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
labels = ['A', 'S', 'T']
sizes = []
sizes.append(list(cleaned_data['Drug'].value_counts())[0])
sizes.append(list(cleaned_data['Drug'].value_counts())[1])
sizes.append(list(cleaned_data['Drug'].value_counts())[2])
explode = (0, 0.1, 0)
colors = ['#ffcc99', '#66b3ff', '#ff9999']
plt.axis('equal')
plt.tight_layout()
from pandas.plotting import scatter_matrix
fig, ax = plt.subplots(figsize=(12,12))
scatter_matrix(cleaned_data, alpha=1, ax=ax)
df_plot = cleaned_data[cleaned_data['Diff'] > 0]
plt.figure(figsize=(10, 6))
ax = sns.boxplot(x='Drug', y='Mem_Score_After', hue='Drug', data=cleaned_data, palette='Set3') | code |
33108543/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1], x='Mem_Score_Before', y='first_name', title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show() | code |
33108543/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
plt.figure(figsize=(16, 6))
sns.barplot(x='Drug', y='Mem_Score_Before', data=cleaned_data, order=cleaned_data.Drug.unique().tolist())
plt.title('Distribution of Drugs') | code |
33108543/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.head() | code |
33108543/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
df_plot = cleaned_data[cleaned_data['Diff'] > 0]
sns.boxplot('AgeRange', 'Diff', data=df_plot) | code |
33108543/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.describe() | code |
33108543/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
cleaned_data.Drug.unique() | code |
33108543/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
cleaned_data | code |
33108543/cell_18 | [
"text_html_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
labels = ['A', 'S', 'T']
sizes = []
sizes.append(list(cleaned_data['Drug'].value_counts())[0])
sizes.append(list(cleaned_data['Drug'].value_counts())[1])
sizes.append(list(cleaned_data['Drug'].value_counts())[2])
explode = (0, 0.1, 0)
colors = ['#ffcc99', '#66b3ff', '#ff9999']
plt.axis('equal')
plt.tight_layout()
from pandas.plotting import scatter_matrix
fig, ax = plt.subplots(figsize=(12, 12))
scatter_matrix(cleaned_data, alpha=1, ax=ax) | code |
33108543/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
labels = ['A', 'S', 'T']
sizes = []
sizes.append(list(cleaned_data['Drug'].value_counts())[0])
sizes.append(list(cleaned_data['Drug'].value_counts())[1])
sizes.append(list(cleaned_data['Drug'].value_counts())[2])
explode = (0, 0.1, 0)
colors = ['#ffcc99', '#66b3ff', '#ff9999']
plt.figure(figsize=(15, 10))
plt.title('Distribution of Drug', fontsize=20)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)
plt.axis('equal')
plt.tight_layout() | code |
33108543/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_After', y='first_name',
title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show()
sns.pairplot(cleaned_data) | code |
33108543/cell_14 | [
"text_html_output_2.png",
"text_html_output_3.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1],
x='Mem_Score_Before', y='first_name',
title='Patient with Higest Mem_Score_Before', text='Mem_Score_Before', orientation='h')
fig.show()
fig = px.bar(cleaned_data.sort_values('age', ascending=False)[:10][::-1], x='Mem_Score_After', y='first_name', title='Patient with Higest Mem_Score_After', text='Mem_Score_After', orientation='h')
fig.show() | code |
33108543/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x='age', y='Mem_Score_Before', title='Mem_Score_Before over Age', color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x='age', y='Mem_Score_After', title='Mem_Score_After over Age', log_y=True, color_discrete_sequence=['#F42272'])
fig.show() | code |
33108543/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
color_discrete_sequence=['#F42272'])
fig.show()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_After", title="Mem_Score_After over Age",
log_y=True, color_discrete_sequence=['#F42272'])
fig.show()
cleaned_data.Drug.unique()
fig = px.sunburst(cleaned_data.sort_values(by='age', ascending=False).reset_index(drop=True), path=['first_name'], values='Mem_Score_Before', height=700, title='Sunburst for Mem_Score_Before ', color_discrete_sequence=px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig.show() | code |
33108543/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.info() | code |
130025642/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].isnull()]
df_missing.count()
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_train.isnull().sum()
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
df = df_train
import re
feature_names = df.columns
pattern = '[\\[\\]<>]'
new_feature_names = []
for name in feature_names:
new_name = re.sub(pattern, '_', name)
new_feature_names.append(new_name)
df.columns = new_feature_names
df.columns
df = df.dropna(subset=['x_e_out _-_'])
X = df.drop('x_e_out _-_', axis=1)
y = df['x_e_out _-_']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Root Mean squared error: {np.sqrt(mse):.2f}') | code |
130025642/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)
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].isnull()]
df_missing.count()
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_train.isnull().sum() | code |
130025642/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)
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.info() | code |
130025642/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)
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum() | code |
130025642/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 |
130025642/cell_8 | [
"application_vnd.jupyter.stderr_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/playground-series-s3e15/data.csv')
df.columns | code |
130025642/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt | code |
130025642/cell_17 | [
"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('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].isnull()]
df_missing.count() | code |
130025642/cell_14 | [
"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/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum() | code |
130025642/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].isnull()]
df_missing.count()
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_test = df_missing.drop('x_e_out [-]', axis=1)
df_train = df_non_missing.drop('id', axis=1)
df_train.isnull().sum()
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
df = df_train
import re
feature_names = df.columns
pattern = '[\\[\\]<>]'
new_feature_names = []
for name in feature_names:
new_name = re.sub(pattern, '_', name)
new_feature_names.append(new_name)
df.columns = new_feature_names
df.columns | code |
130025642/cell_10 | [
"application_vnd.jupyter.stderr_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
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, cmap='coolwarm', annot=True)
plt.title('Correlation Matrix')
plt.show() | code |
130025642/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)
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.describe() | code |
130025642/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('/kaggle/input/playground-series-s3e15/data.csv')
df.head() | code |
106212685/cell_21 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb['last review'] = pd.to_datetime(abnb['last review'])
abnb['Construction year'] = pd.to_datetime(abnb['Construction year'])
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['review_rate_number'].unique()
print('\n')
abnb['review_rate_number'].value_counts()
print('\n')
abnb['rating'] = pd.cut(abnb['review_rate_number'], bins=[0, 1, 2, 3, 4, 5], labels=['One-Star', 'Two-Star', 'Three-Star', 'Four-Star', 'Five-Star'], include_lowest=True)
abnb['rating'] = abnb['rating'].cat.add_categories('missing values').fillna('missing values')
abnb[['review_rate_number', 'rating']].head(5) | code |
106212685/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape | code |
106212685/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum() | code |
106212685/cell_25 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['cancellation_policy'].unique()
print('\n')
abnb['cancellation_policy'].value_counts() | code |
106212685/cell_34 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['price'] = abnb['price'].str.replace('$', '').str.replace(' ', '').str.replace(',', '').astype(float)
abnb['price'].head(5) | code |
106212685/cell_23 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['license'].unique()
print('\n')
abnb['license'].value_counts() | code |
106212685/cell_33 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb[['price', 'service_fee']].head(5) | code |
106212685/cell_44 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.columns
abnb.columns
'Present memory: {} '.format(abnb.memory_usage().sum())
print()
gc.collect() | code |
106212685/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns | code |
106212685/cell_40 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
def monthQ(month):
if 0 < month <= 3:
return 1
if 3 < month <= 6:
return 2
if 6 < month <= 9:
return 3
if 9 < month <= 12:
return 4
abnb['last_review'].head(3)
abnb['last_reviewed_year'] = abnb['last_review'].dt.year
abnb['last_reviewed_month'] = abnb['last_review'].dt.month
abnb['last_reviewed_day'] = abnb['last_review'].dt.day
print('\n')
abnb['quarterly_review'] = abnb['last_reviewed_month'].map(lambda m: monthQ(m))
print('\n')
abnb[['last_review', 'last_reviewed_year', 'last_reviewed_month', 'last_reviewed_day', 'quarterly_review']].head(5) | code |
106212685/cell_29 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['instant_bookable'].unique()
print('\n')
abnb['instant_bookable'].value_counts() | code |
106212685/cell_48 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.columns
abnb.columns
'Present memory: {} '.format(abnb.memory_usage().sum())
gc.collect()
abnb.drop(columns=['lat', 'long', 'cancellation_policy', 'room_type', 'license'], inplace=True)
'Current memory usage: {} '.format(abnb.memory_usage().sum())
grpd = abnb.groupby(['host_id'])
grpd | code |
106212685/cell_41 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.columns | code |
106212685/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull() | code |
106212685/cell_19 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.info() | code |
106212685/cell_18 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns | code |
106212685/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.