path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18112986/cell_10 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
122258235/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
from sklearn.linear_model import LinearRegression
df = LinearRegression()
df.fit(X_train, y_train)
predictions = df.predict(X_test)
df.predict([[5.1, 2.5... | code |
122258235/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
print('Distinct values for species', df['Species'].unique()) | code |
122258235/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df.describe() | code |
122258235/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df.head() | code |
122258235/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
from sklearn.linear_model import LinearRegression
df = LinearRegression()
df.fit(X_train, y_train)
predictions = df.predict(X_test)
print('coefficient of... | code |
122258235/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
print('Maximum number of the value for sepal_lengte', df['SepalLengthCm'].max()) | code |
122258235/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
from sklearn.linear_model import LinearRegression
df = LinearRegression()
df.fit(X_train, y_train)
predictions = df.predict(X_test)
df.predict([[5.1, 2.5... | code |
122258235/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error
import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
from sklearn.linear_model import LinearRegression
df = LinearRegression()
df.fit(X_tr... | code |
122258235/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
from sklearn.linear_model import LinearRegression
df = LinearRegression()
df.fit(X_train, y_train)
predictions = df.predict(X_test)
df.predict([[5.1, 2.5... | code |
122258235/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
from pathlib import Path
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
print(df['Species'].value_counts()) | code |
2012154/cell_42 | [
"text_plain_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_ma... | code |
2012154/cell_21 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import numpy as np
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
info = pd.pivot_table(data_df, index=['Brand Name'], values=['Rating', 'Revie... | code |
2012154/cell_25 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
import matplotlib.pyplot as plt
ylabel = data_df['Price']
xlabel... | code |
2012154/cell_23 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
import matplotlib.pyplot as plt
ylabel = data_df['Price']
plt.yl... | code |
2012154/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_55 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_74 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import numpy as np
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
info = pd.pivot_table(data_df, index=['Brand Name'], values=['Rating', 'Revie... | code |
2012154/cell_76 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.utils import shuffle
import numpy as np
import pandas as pd
import pylab
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
info = pd.pivot_table(data_df, index=['Brand Name'], values=['R... | code |
2012154/cell_40 | [
"text_html_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_ma... | code |
2012154/cell_29 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False) | code |
2012154/cell_39 | [
"text_plain_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1) | code |
2012154/cell_65 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_ma... | code |
2012154/cell_61 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_19 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
data_df.describe() | code |
2012154/cell_69 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
product_name = []
for item in data['Product Name']:
if item in product_name:
continue
else:
product_name.append(item)
len(product_name) | code |
2012154/cell_45 | [
"text_plain_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_ma... | code |
2012154/cell_51 | [
"text_plain_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_ma... | code |
2012154/cell_62 | [
"text_html_output_1.png"
] | from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
imp... | code |
2012154/cell_59 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_28 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
import matplotlib.pyplot as plt
ylabel = data_df['Price']
xlabel... | code |
2012154/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df[:10] | code |
2012154/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_31 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2012154/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df.head() | code |
2012154/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data['Brand Name']
brands = []
for item in data['Brand Name']:
if item in brands:
continue
else:
brands.append(item)
len(brands) | code |
2012154/cell_71 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import numpy as np
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
info = pd.pivot_table(data_df, index=['Brand Name'], values=['Rating', 'Revie... | code |
2012154/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data.head() | code |
2012154/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
data_file = '../input/Amazon_Unlocked_Mobile.csv'
data = pd.read_csv(data_file)
data_df = pd.DataFrame(data)
data_df = shuffle(data_df)
data_df = data_df.dropna()
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = da... | code |
2019757/cell_4 | [
"text_plain_output_1.png"
] | df.groupby(['country'])['quality_of_education'].mean()
a = df.groupby(['country'])['quality_of_education'].mean()
a.sort_values(ascending=False) | code |
2019757/cell_6 | [
"text_plain_output_1.png"
] | df.groupby(['country'])['quality_of_education'].mean()
a = df.groupby(['country'])['quality_of_education'].mean()
a.sort_values(ascending=False)
koolid = df.groupby(['country'])['year'].count()
koolid.sort_values(ascending=False)
a2015 = df[df['year'] == 2015]
koolid2015 = a2015.groupby(['country'])['year'].count()
... | code |
2019757/cell_2 | [
"text_html_output_1.png"
] | df[df['country'] == 'Estonia'] | code |
2019757/cell_3 | [
"text_plain_output_1.png"
] | df.groupby(['country'])['quality_of_education'].mean() | code |
2019757/cell_5 | [
"text_plain_output_1.png"
] | df.groupby(['country'])['quality_of_education'].mean()
a = df.groupby(['country'])['quality_of_education'].mean()
a.sort_values(ascending=False)
koolid = df.groupby(['country'])['year'].count()
koolid.sort_values(ascending=False) | code |
17122451/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
list(train_df.columns.values)
train_df.isna().sum() | code |
17122451/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
train_df.head() | code |
17122451/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17122451/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
list(train_df.columns.values) | code |
17122451/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
list(train_df.columns.values)
train_df.isna().sum()
from sklearn.preprocessing import LabelEncoder
labelEncoder = LabelEncoder()
train_df.team_1... | code |
89133672/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape | code |
89133672/cell_29 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape
df.isnull().sum()
blacklist_index = df[~df.market_cap.str.conta... | code |
89133672/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.head() | code |
89133672/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
TITLE_SIZE = 20
TITLE_PAD = 15
LABELE_SIZE = 15
LABELE_PAD = 10
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'c... | code |
89133672/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
TITLE_SIZE = 20
TITLE_PAD = 15
LABELE_SIZE = 15
LABELE_PAD = 10
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'c... | code |
89133672/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
TITLE_SIZE = 20
TITLE_PAD = 15
LABELE_SIZE = 15
LABELE_PAD = 10
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'c... | code |
89133672/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape
df.isnull().sum() | code |
34121718/cell_63 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_56 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #collection of command style functions that make matplotlib work
import numpy as np
import pandas as pd
import seaborn as sns #statistical data visualization.
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazi... | code |
34121718/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_55 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_65 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt #collection of command style functions that make matplotlib work
import numpy as np
import pandas as pd
import seaborn as sns #statistical data visualization.
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazi... | code |
34121718/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_51 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_62 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_59 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #collection of command style functions that make matplotlib work
import numpy as np
import pandas as pd
import seaborn as sns #statistical data visualization.
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazi... | code |
34121718/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = p... | code |
34121718/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
34121718/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
orders_data = pd.read_csv('../input/brazilian-ecommerce/olist_orders_dataset.csv')
payment_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_payments_dataset.csv')
review_data = pd.read_csv('../input/brazilian-ecommerce/olist_order_reviews_dataset.csv')
items_data = pd.read_csv('../input... | code |
2033003/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
print(haberman.columns) | code |
2033003/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman.plot()
plt.show() | code |
2033003/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
haberman.head() | code |
2033003/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2033003/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman.hist()
plt.show() | code |
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