path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
129005932/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
from sklearn.preprocessing import LabelEncoder
labelencoder_cut = LabelEncoder()
train['cut'] = labelencoder_cut.fit_transform(train['cut'])
labelencoder_color = LabelEncoder()
train['color'] = labelencoder_color.fit_transform(train['color'])
labelencoder_clarity = LabelEncoder()
train['clarity'] = labelencoder_clarity.fit_transform(train['clarity'])
plt.figure(figsize=(20, 15))
correlations = train.corr()
sns.heatmap(correlations, cmap='coolwarm', annot=True)
plt.show() | code |
129005932/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
sns.boxplot(x=data['carat']) | code |
129005932/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum() | code |
129005932/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
plt.hist(train['price']) | code |
129005932/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
from sklearn.preprocessing import LabelEncoder
labelencoder_cut = LabelEncoder()
train['cut'] = labelencoder_cut.fit_transform(train['cut'])
labelencoder_color = LabelEncoder()
train['color'] = labelencoder_color.fit_transform(train['color'])
labelencoder_clarity = LabelEncoder()
train['clarity'] = labelencoder_clarity.fit_transform(train['clarity'])
train.info() | code |
129005932/cell_1 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train | code |
129005932/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
sns.boxplot(x=data['carat']) | code |
129005932/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
from sklearn.preprocessing import LabelEncoder
labelencoder_cut = LabelEncoder()
train['cut'] = labelencoder_cut.fit_transform(train['cut'])
labelencoder_color = LabelEncoder()
train['color'] = labelencoder_color.fit_transform(train['color'])
labelencoder_clarity = LabelEncoder()
train['clarity'] = labelencoder_clarity.fit_transform(train['clarity'])
correlations = train.corr()
sns.scatterplot(x=X.ravel(), y=y) | code |
129005932/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.describe() | code |
129005932/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
train.info() | code |
129005932/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
Q1 = train['carat'].quantile(0.25)
Q3 = train['carat'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['carat'] >= Q1 - 1.5 * IQR) & (train['carat'] <= Q3 + 1.5 * IQR)]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
from sklearn.preprocessing import LabelEncoder
labelencoder_cut = LabelEncoder()
train['cut'] = labelencoder_cut.fit_transform(train['cut'])
labelencoder_color = LabelEncoder()
train['color'] = labelencoder_color.fit_transform(train['color'])
labelencoder_clarity = LabelEncoder()
train['clarity'] = labelencoder_clarity.fit_transform(train['clarity'])
sns.barplot(data=train, x='color', y='price') | code |
129005932/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
train
train.isnull().sum()
plt.hist(train['carat']) | code |
18102076/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
y = clean_data[['high_humidity_label']].copy()
y
y.head() | code |
18102076/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
print(after_rows) | code |
18102076/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
morning_features = ['air_pressure_9am', 'air_temp_9am', 'avg_wind_direction_9am', 'avg_wind_speed_9am', 'max_wind_direction_9am', 'max_wind_speed_9am', 'rain_accumulation_9am', 'rain_duration_9am']
X = clean_data[morning_features].copy()
X.columns | code |
18102076/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns | code |
18102076/cell_34 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
humidity_classifier.fit(X_train, y_train)
type(humidity_classifier) | code |
18102076/cell_30 | [
"text_plain_output_1.png"
] | X_train.head() | code |
18102076/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
humidity_classifier.fit(X_train, y_train) | code |
18102076/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
clean_data['relative_humidity_3pm'].head() | code |
18102076/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data.head() | code |
18102076/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
humidity_classifier.fit(X_train, y_train)
predictions = humidity_classifier.predict(X_test)
accuracy_score(y_true=y_test, y_pred=predictions) | code |
18102076/cell_29 | [
"text_html_output_1.png"
] | print(type(X_train), type(X_test), type(y_train), type(y_test)) | code |
18102076/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
y = clean_data[['high_humidity_label']].copy()
y
y.columns | code |
18102076/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
print(before_rows) | code |
18102076/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
y = clean_data[['high_humidity_label']].copy()
y | code |
18102076/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)] | code |
18102076/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
before_rows - after_rows | code |
18102076/cell_38 | [
"text_plain_output_1.png"
] | y_test['high_humidity_label'][:10] | code |
18102076/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/daily_weather.csv')
data.columns
data[data.isnull().any(axis=1)]
before_rows = data.shape[0]
data = data.dropna()
after_rows = data.shape[0]
clean_data = data.copy()
humidity_level = 24.99
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > humidity_level) * 1
print(clean_data['high_humidity_label']) | code |
18102076/cell_31 | [
"text_plain_output_1.png"
] | y_train.head() | code |
18102076/cell_37 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
humidity_classifier.fit(X_train, y_train)
predictions = humidity_classifier.predict(X_test)
predictions[:10] | code |
329676/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.info() | code |
329676/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
battles_df = pd.read_csv('../input/battles.csv')
sns.countplot(x='attacker_king', data=battles_df, hue='attacker_outcome') | code |
329676/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.drop('year', axis=1, inplace=True)
battles_df.drop(['name', 'battle_number', 'note'], axis=1, inplace=True)
battles_df.info() | code |
329676/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.drop('year', axis=1, inplace=True)
battles_df.drop(['name', 'battle_number', 'note'], axis=1, inplace=True)
pattern = '[a-z][0-9]'
test = battles_df[~battles_df.attacker_2.isnull()].attacker_2.replace(pattern, 1, regex=True)
battles_df.attacker_2.fillna(0, inplace=True)
battles_df.attacker_3.fillna(0, inplace=True)
battles_df.attacker_4.fillna(0, inplace=True)
def find_attacker_allies(my_data):
col2 = my_data['attacker_2']
col3 = my_data['attacker_3']
col4 = my_data['attacker_4']
number = 0
if col2 != 0:
number = number + 1
if col3 != 0:
number = number + 1
if col4 != 0:
number = number + 1
return number
battles_df['attacker_allies'] = battles_df.apply(find_attacker_allies, axis=1)
battles_df.drop(['attacker_2', 'attacker_3', 'attacker_4'], axis=1, inplace=True)
battles_df.defender_2.fillna(0, inplace=True)
battles_df.defender_3.fillna(0, inplace=True)
battles_df.defender_4.fillna(0, inplace=True)
def find_defender_allies(my_data):
col2 = my_data['defender_2']
col3 = my_data['defender_3']
col4 = my_data['defender_4']
number = 0
if col2 != 0:
number = number + 1
if col3 != 0:
number = number + 1
if col4 != 0:
number = number + 1
return number
battles_df['defender_allies'] = battles_df.apply(find_defender_allies, axis=1)
battles_df.drop(['defender_2', 'defender_3', 'defender_4'], axis=1, inplace=True)
battles_df.head() | code |
329676/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
battles_df = pd.read_csv('../input/battles.csv')
sns.barplot(x='year', y='attacker_outcome', data=battles_df) | code |
329676/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.head() | code |
329676/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.drop('year', axis=1, inplace=True)
battles_df.drop(['name', 'battle_number', 'note'], axis=1, inplace=True)
battles_df.attacker_2.head() | code |
329676/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
battles_df = pd.read_csv('../input/battles.csv')
battles_df.attacker_outcome.head() | code |
129017471/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train
train.isnull().sum() | code |
129017471/cell_2 | [
"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 pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train | code |
129017471/cell_11 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
clf = tree.DecisionTreeClassifier(random_state=8, max_depth=3)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129017471/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 |
129017471/cell_7 | [
"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
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
Q1 = train['fc'].quantile(0.25)
Q3 = train['fc'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['fc'] >= Q1 - 1.5 * IQR) & (train['fc'] <= Q3 + 1.5 * IQR)]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
sns.boxplot(x=data['fc']) | code |
129017471/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)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train
train.isnull().sum()
Q1 = train['fc'].quantile(0.25)
Q3 = train['fc'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['fc'] >= Q1 - 1.5 * IQR) & (train['fc'] <= Q3 + 1.5 * IQR)]
train.info() | code |
129017471/cell_3 | [
"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 pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train
train.describe() | code |
129017471/cell_5 | [
"text_plain_output_1.png",
"image_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
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = train
sns.boxplot(x=data['fc']) | code |
88105260/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 |
88105260/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | csv = pandas.read_csv('../input/hourly-dataset-of-vehicle-for-traffic-analysis/data.csv') | code |
73065634/cell_2 | [
"text_plain_output_1.png"
] | import urllib.request
url = urllib.request.urlopen('https://www.reddit.com/r/todayilearned/top.json?limit=100')
text = url.read().decode()
destination = open('json_data.json', 'w')
destination.write(text) | code |
73065634/cell_3 | [
"text_plain_output_1.png"
] | import json
import json
f = open('json_data.json', 'r')
copy_from_disk = json.load(f)
for index in range(len(copy_from_disk['data']['children'])):
print(copy_from_disk['data']['children'][index]['data']['title']) | code |
16154305/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(10, 10))
sns.jointplot('population', 'GDP', data=df, kind='reg', color='green') | code |
16154305/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
df.GVA_main.value_counts().iplot(kind='bar', colors='green') | code |
16154305/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(10, 8))
sns.boxplot(x='GDP_per_capita', y='GVA_main', palette=p, data=df) | code |
16154305/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
brazil['GVA_MAIN'].value_counts() | code |
16154305/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum() | code |
16154305/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
df.head() | code |
16154305/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(9, 6))
sns.distplot(df.HDI, color='lightseagreen') | code |
16154305/cell_1 | [
"text_html_output_3.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.tools as tls
import plotly.plotly as py
from plotly.offline import init_notebook_mode
import plotly.graph_objs as go
import cufflinks as cf
import colorlover as cl
from IPython.display import HTML
import folium
init_notebook_mode(connected=True)
cf.go_offline()
p = 'BuGn_r'
cls = cl.scales['9']['seq']['BuGn']
sns.set()
HTML(cl.to_html(cls)) | code |
16154305/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(9, 6))
sns.distplot(df.education_index, color='seagreen') | code |
16154305/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
brazil.head() | code |
16154305/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(9, 6))
sns.distplot(df.life_expect_index, color='mediumseagreen') | code |
16154305/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
plt.figure(figsize=(10, 10))
sns.pairplot(df[['GDP', 'HDI', 'education_index', 'life_expect_index', 'GVA_main']], hue='GVA_main', palette=p) | code |
16154305/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
brazil = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
features = ['CITY', 'STATE', 'CAPITAL', 'IDHM', 'IDHM_Longevidade', 'IDHM_Educacao', 'LONG', 'LAT', 'ALT', 'AREA', 'RURAL_URBAN', 'GVA_AGROPEC', 'GVA_INDUSTRY', 'GVA_SERVICES', 'GVA_PUBLIC', 'GDP', 'POP_GDP', 'GDP_CAPITA', 'GVA_MAIN']
df = brazil[features]
pd.isnull(df).sum()
df.dropna(inplace=True)
df.columns = ['city', 'state', 'capital', 'HDI', 'life_expect_index', 'education_index', 'longitude', 'latitude', 'altitude', 'area', 'typology', 'GVA_agriculture', 'GVA_industry', 'GVA_services', 'GVA_public_services', 'GDP', 'population', 'GDP_per_capita', 'GVA_main']
GVA_main_dict = {'Administração, defesa, educação e saúde públicas e seguridade social': 'Public Services', 'Demais serviços': 'Other services', 'Agricultura, inclusive apoio à agricultura e a pós colheita': 'Agriculture', 'Indústrias de transformação': 'Manufacturing Industries', 'Pecuária, inclusive apoio à pecuária': 'Livestock', 'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação': 'Public Utilities', 'Comércio e reparação de veículos automotores e motocicletas': 'Vehicle sales & repair', 'Indústrias extrativas': 'Extractive Industries', 'Produção florestal, pesca e aquicultura': 'Forestry & Aquaculture', 'Construção': 'Construction'}
df.GVA_main = df.GVA_main.map(GVA_main_dict)
typology_dict = {'Rural Adjacente': 'Adjacent Rural', 'Urbano': 'Urban', 'Intermediário Adjacente': 'Adjacent Intermediate', 'Rural Remoto': 'Remote Rural', 'Intermediário Remoto': 'Remote Intermediate'}
df.typology = df.typology.map(typology_dict)
df.info() | code |
73075985/cell_9 | [
"image_output_1.png"
] | from sklearn import model_selection
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn import pipeline
from sklearn import model_selection
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.figsize': (8.0, 6.0)})
plt.rcParams.update({'font.size': 11})
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum())
train['kfold'] = -1
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)
for fold, (train_indices, valid_indices) in enumerate(kf.split(X=train)):
train.iloc[valid_indices, -1] = fold
def plot_folds(fold, data):
pass
sns.set_style('whitegrid')
fig, ax = plt.subplots(1, 5, figsize=(20, 4), sharey=True)
for i in range(0, 5):
plot_folds(i, train[train.kfold == i].target)
plt.show() | code |
73075985/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
73075985/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn import pipeline
from sklearn import model_selection
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.figsize': (8.0, 6.0)})
plt.rcParams.update({'font.size': 11})
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum())
train['target'].hist()
plt.title('Target value distribution')
plt.show() | code |
73075985/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum()) | code |
2009464/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_digit_vals = {}
def convert_to_int(val):
return text_digit_vals[val]
if df[col].dtype != np.int64 and df[col].dtype != np.float64:
column_contents = df[col].values.tolist()
unique_elements = set(column_contents)
x = 0
for unique in unique_elements:
if unique not in text_digit_vals:
text_digit_vals[unique] = x
x += 1
df[col] = list(map(convert_to_int, df[col]))
return df
df_o = pd.read_csv('../input/mushrooms.csv') | code |
2009464/cell_3 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing,cross_validation,neighbors
from sklearn.cluster import KMeans
import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_digit_vals = {}
def convert_to_int(val):
return text_digit_vals[val]
if df[col].dtype != np.int64 and df[col].dtype != np.float64:
column_contents = df[col].values.tolist()
unique_elements = set(column_contents)
x = 0
for unique in unique_elements:
if unique not in text_digit_vals:
text_digit_vals[unique] = x
x += 1
df[col] = list(map(convert_to_int, df[col]))
return df
df_o = pd.read_csv('../input/mushrooms.csv')
df = handle_non_numeric(df_o)
X = np.array(df.drop(['class'], 1).astype(float))
X = preprocessing.scale(X)
y = np.array(df['class'])
clf = KMeans(n_clusters=2)
clf.fit(X)
correct = 0
for i in range(len(X)):
predict_me = np.array(X[i].astype(float))
predict_me = predict_me.reshape(-1, len(predict_me))
prediction = clf.predict(predict_me)
if prediction[0] == y[i]:
correct += 1
print('accuracy', correct / len(X)) | code |
2009464/cell_5 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing,cross_validation,neighbors
from sklearn.cluster import KMeans
import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_digit_vals = {}
def convert_to_int(val):
return text_digit_vals[val]
if df[col].dtype != np.int64 and df[col].dtype != np.float64:
column_contents = df[col].values.tolist()
unique_elements = set(column_contents)
x = 0
for unique in unique_elements:
if unique not in text_digit_vals:
text_digit_vals[unique] = x
x += 1
df[col] = list(map(convert_to_int, df[col]))
return df
df_o = pd.read_csv('../input/mushrooms.csv')
df = handle_non_numeric(df_o)
X = np.array(df.drop(['class'], 1).astype(float))
X = preprocessing.scale(X)
y = np.array(df['class'])
clf = KMeans(n_clusters=2)
clf.fit(X)
correct = 0
for i in range(len(X)):
predict_me = np.array(X[i].astype(float))
predict_me = predict_me.reshape(-1, len(predict_me))
prediction = clf.predict(predict_me)
if prediction[0] == y[i]:
correct += 1
df = handle_non_numeric(df_o)
X = np.array(df.drop(['class'], 1).astype(float))
X = preprocessing.scale(X)
y = np.array(df['class'])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
clf = neighbors.KNeighborsClassifier()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print('accuracy', accuracy) | code |
128018504/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.displot(data=wdi_data, x='Life expectancy, female', aspect=2) | code |
128018504/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
wdi_data['High Income Economy'].value_counts() | code |
128018504/cell_9 | [
"image_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
wdi_data['GNI per capita'] = (wdi_data['GNI'] / wdi_data['Population']).round(2)
wdi_data['GNI per capita'] | code |
128018504/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.displot(data=wdi_data, kind='kde', x='Life expectancy, female', hue='High Income Economy', aspect=2) | code |
128018504/cell_4 | [
"image_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data | code |
128018504/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.displot(data=wdi_data, x='Life expectancy, female', col='Region') | code |
128018504/cell_44 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.relplot(data=wdi_data, x='GNI per capita', y='Life expectancy, female', aspect=2) | code |
128018504/cell_6 | [
"image_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
wdi_data.info() | code |
128018504/cell_29 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.catplot(data=wdi_data, kind='box', x='Life expectancy, female', y='Region', hue='High Income Economy', aspect=2) | code |
128018504/cell_48 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.jointplot(kind='hex', data=wdi_data, x='Physicians', y='Life expectancy, female') | code |
128018504/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
wdi_data['Region'].value_counts() | code |
128018504/cell_50 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.pairplot(data=wdi_data, x_vars=['Women in national parliament', 'Tertiary education, female', 'Greenhouse gas emissions'], y_vars='Life expectancy, female', aspect=2) | code |
128018504/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
pd.crosstab(wdi_data['High Income Economy'], wdi_data['Region']) | code |
128018504/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2) | code |
128018504/cell_31 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.catplot(data=wdi_data, kind='violin', x='Life expectancy, female', y='Region', hue='High Income Economy', split=True, aspect=2) | code |
128018504/cell_46 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.relplot(data=wdi_data, x='GNI per capita', y='Life expectancy, female', hue='Region', aspect=2) | code |
128018504/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wdi_data = pd.read_csv('/kaggle/input/world-demographic-indicators-extract/wdi_wide.csv')
wdi_data
features = ['Internet use', 'International tourism', 'Life expectancy, female', 'Life expectancy, male', 'Physicians', 'Population', 'Women in national parliament', 'Tertiary education, female', 'Tertiary education, male', 'Greenhouse gas emissions', 'High Income Economy', 'GNI per capita']
wdi_data.groupby('High Income Economy')[features].mean().round(2)
sns.catplot(data=wdi_data, kind='box', x='Life expectancy, female', y='Region', aspect=2) | code |
32065949/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.columns = [c.replace(' ', '_') for c in df.columns]
df.drop(columns=['Name_', 'Profile', 'Profession_', 'Location', 'Prority__level_1', 'Priority_level_2', 'Priority_level_3'], inplace=True)
df['Type_of_Location_'][0] = 1
df['Type_of_Location_'] = pd.to_numeric(df['Type_of_Location_'])
df.info() | code |
32065949/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_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.columns = [c.replace(' ', '_') for c in df.columns]
df.head(1) | code |
32065949/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.columns = [c.replace(' ', '_') for c in df.columns]
df.drop(columns=['Name_', 'Profile', 'Profession_', 'Location', 'Prority__level_1', 'Priority_level_2', 'Priority_level_3'], inplace=True)
df['Type_of_Location_'][0] = 1
df['Type_of_Location_'] = pd.to_numeric(df['Type_of_Location_'])
from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3).fit(df)
df1 = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
clustering = pd.DataFrame({'Name': df1['Name '], 'Clusters': k_means.labels_})
clustering.to_csv('/kaggle/working/clustering_output.csv', index=False)
cl = pd.read_csv('/kaggle/working/clustering_output.csv')
cl.head() | code |
32065949/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.columns = [c.replace(' ', '_') for c in df.columns]
df.drop(columns=['Name_', 'Profile', 'Profession_', 'Location', 'Prority__level_1', 'Priority_level_2', 'Priority_level_3'], inplace=True)
df.head(2) | code |
32065949/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.columns = [c.replace(' ', '_') for c in df.columns]
df.drop(columns=['Name_', 'Profile', 'Profession_', 'Location', 'Prority__level_1', 'Priority_level_2', 'Priority_level_3'], inplace=True)
df.info() | code |
32065949/cell_3 | [
"text_html_output_1.png"
] | import os
import warnings
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))
import warnings
warnings.filterwarnings('ignore') | code |
32065949/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/social-profile-of-customers/Social profile of customers_without header.xlsx')
df.head() | code |
16146132/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (12, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
corrplot = sns.heatmap(df_train.corr(), cmap=plt.cm.Reds, annot=True) | code |
16146132/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.info() | code |
16146132/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min() | code |
16146132/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
print('{} numerical features:\n{} \nand {} categorical features:\n{}'.format(len(num_features), num_features, len(cat_features), cat_features)) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.