path stringlengths 13 17 | screenshot_names listlengths 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 = p... | 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... | 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... | 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.b... | 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... | 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) & (t... | 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.py... | 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'] > ... | 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'] > ... | 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'] > ... | 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'] > ... | 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'] > ... | 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'] > ... | 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... | 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
tr... | 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'].... | 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()
imp... | 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_SER... | 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', 'G... | 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_SER... | 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', 'G... | 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', 'G... | 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_SER... | 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
in... | 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_SER... | 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_SER... | 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_SER... | 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', 'G... | 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_select... | 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 ... | 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 ... | 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):... | 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):... | 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 e... | 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 e... | 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 e... | 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 e... | 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 e... | 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 e... | 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 e... | 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', 'Ter... | 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 e... | 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 e... | 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 e... | 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... | 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', 'Profe... | 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... | 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... | 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(... | 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()
ca... | code |
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