path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
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 = []
for col in features:
Q1 = np.percentile(df[col].dropna(), 25)
Q3 = np.percentile(df[col].dropna(), 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outliers_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outliers_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
outliers_to_drop = detect_outlier(train, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
train.loc[outliers_to_drop]
train = train.drop(outliers_to_drop, axis=0).reset_index(drop=True)
train.info()
train.isnull().sum() | 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 = []
for col in features:
Q1 = np.percentile(df[col].dropna(), 25)
Q3 = np.percentile(df[col].dropna(), 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outliers_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outliers_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
outliers_to_drop = detect_outlier(train, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
train.loc[outliers_to_drop]
train = train.drop(outliers_to_drop, axis=0).reset_index(drop=True)
train.isnull().sum()
train.dtypes | 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 = []
for col in features:
Q1 = np.percentile(df[col].dropna(), 25)
Q3 = np.percentile(df[col].dropna(), 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outliers_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outliers_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
outliers_to_drop = detect_outlier(train, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
train.loc[outliers_to_drop] | 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, 1.1]])
df.predict([[7.5, 3.0, 1.8]]) | 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 determination:', df.score(X_train, y_train))
print('intercept:', df.intercept_)
print('slope:', df.coef_) | 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, 1.1]])
df.predict([[7.5, 3.0, 1.8]])
df.predict([[4.6, 3.5, 0.2]]) | 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_train, y_train)
predictions = df.predict(X_test)
from sklearn.metrics import mean_squared_error, mean_absolute_error
print('mean_squared_error : ', mean_squared_error(y_test, predictions))
print('mean_absolute_error : ', mean_absolute_error(y_test, predictions)) | 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, 1.1]]) | 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_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
sample2 = data_df['Reviews'][9001]
print(sample2)
print('Sentiment: ')
print(sentiment_value(sample2)) | 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', 'Review Votes'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
info = info.sort_values(by=('sum', 'Rating'), ascending=False)
info.head(10) | 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 = data_df['Rating']
ylabel2 = data_df['Price']
plt.ylabel('Price')
xlabel2 = data_df['Review Votes']
plt.xlabel('Review Votes')
plt.scatter(xlabel2, ylabel2, alpha=0.1)
plt.show() | 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.ylabel('Price')
plt.xlabel('Rating')
xlabel = data_df['Rating']
plt.scatter(xlabel, ylabel, alpha=0.1)
plt.show() | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
all_reviews = data_df['Reviews']
all_reviews.head() | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
counter | 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', 'Review Votes'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
info = info.sort_values(by=('sum', 'Rating'), ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
testing2 = pd.pivot_table(temp_data, index=['Brand Name'], values=['Rating', 'Review Votes', 'SENTIMENT_VALUE'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
testing2 = testing2.sort_values(by=('sum', 'Rating'), ascending=False)
testing3 = pd.pivot_table(temp_data, index=['Product Name'], values=['Rating', 'Review Votes', 'SENTIMENT_VALUE'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
testing3 = testing3.sort_values(by=('sum', 'Rating'), ascending=False)
testing3.head(10) | 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=['Rating', 'Review Votes'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
info = info.sort_values(by=('sum', 'Rating'), ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
testing2 = pd.pivot_table(temp_data, index=['Brand Name'], values=['Rating', 'Review Votes', 'SENTIMENT_VALUE'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
testing2 = testing2.sort_values(by=('sum', 'Rating'), ascending=False)
import pylab
names = testing2.index[:10]
y = testing2['sum', 'SENTIMENT_VALUE'][:10]
y2 = testing2['sum', 'Rating'][:10]
pylab.figure(figsize=(15, 7))
x = range(10)
pylab.subplot(2, 1, 1)
pylab.xticks(x, names)
pylab.ylabel('Summed Values')
pylab.title('Total Sum Values')
pylab.plot(x, y, 'r-', x, y2, 'b-')
pylab.legend(['SentimentValue', 'Rating'])
y_new = testing2['mean', 'SENTIMENT_VALUE'][:10]
y2_new = testing2['mean', 'Rating'][:10]
pylab.figure(figsize=(15, 7))
pylab.subplot(2, 1, 2)
pylab.xticks(x, names)
pylab.ylabel('Mean Values')
pylab.title('Mean Values')
pylab.plot(x, y_new, 'r-', x, y2_new, 'b-')
pylab.legend(['SentimentValue', 'Rating'])
pylab.show() | 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_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
sample = data_df['Reviews'][1231]
print(sample)
print('Sentiment: ')
print(sentiment_value(sample)) | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
product_name_20k = []
for item in temp_data['Product Name']:
if item in product_name_20k:
continue
else:
product_name_20k.append(item)
len(product_name_20k) | 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_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
sample1 = data_df['Reviews'][99314]
print(sample1)
print('Sentiment: ')
print(sentiment_value(sample1)) | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
temp_data.head() | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
brands_temp = []
for item in temp_data['Brand Name']:
if item in brands_temp:
continue
else:
brands_temp.append(item)
len(brands_temp) | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
temp_data.head() | 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_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
all_reviews = data_df['Reviews']
data_df = data_df.reset_index(drop=True)
all_reviews = data_df['Reviews']
all_sent_values = []
all_sentiments = []
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
for i in range(0, 20000):
all_sent_values.append(sentiment_value(all_reviews[i]))
len(all_sent_values) | 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_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
all_reviews = data_df['Reviews']
data_df = data_df.reset_index(drop=True)
all_reviews = data_df['Reviews']
all_sent_values = []
all_sentiments = []
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
for i in range(0, 20000):
all_sent_values.append(sentiment_value(all_reviews[i]))
temp_data = data_df[0:20000]
temp_data.shape
SENTIMENT_VALUE = []
SENTIMENT = []
for i in range(0, 20000):
sent = all_sent_values[i]
if sent <= 1 and sent >= 0.5:
SENTIMENT.append('V.Positive')
SENTIMENT_VALUE.append(5)
elif sent < 0.5 and sent > 0:
SENTIMENT.append('Positive')
SENTIMENT_VALUE.append(4)
elif sent == 0:
SENTIMENT.append('Neutral')
SENTIMENT_VALUE.append(3)
elif sent < 0 and sent >= -0.5:
SENTIMENT.append('Negative')
SENTIMENT_VALUE.append(2)
else:
SENTIMENT.append('V.Negative')
SENTIMENT_VALUE.append(1)
temp_data['SENTIMENT_VALUE'] = SENTIMENT_VALUE
temp_data['SENTIMENT'] = SENTIMENT | 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()
import matplotlib.pyplot as plt
ylabel = data_df['Price']
xlabel = data_df['Rating']
ylabel2 = data_df['Price']
xlabel2 = data_df['Review Votes']
ylabel3 = data_df['Rating']
xlabel3 = data_df['Review Votes']
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
all_reviews = data_df['Reviews']
data_df = data_df.reset_index(drop=True)
all_reviews = data_df['Reviews']
all_sent_values = []
all_sentiments = []
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_value(paragraph):
analyser = SentimentIntensityAnalyzer()
result = analyser.polarity_scores(paragraph)
score = result['compound']
return round(score, 1)
for i in range(0, 20000):
all_sent_values.append(sentiment_value(all_reviews[i]))
xaxis = []
for i in range(0, 20000):
xaxis.append(i)
ylabel_new_1 = all_sent_values[:20000]
xlabel = xaxis
plt.figure(figsize=(9, 9))
plt.xlabel('ReviewIndex')
plt.ylabel('SentimentValue(-1 to 1)')
plt.plot(xlabel, ylabel_new_1, 'ro', alpha=0.04)
plt.title('Scatter Intensity Plot of Sentiments')
plt.show() | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
percent_accuracy = accuracy * 100
percent_accuracy | 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 = data_df['Rating']
ylabel2 = data_df['Price']
xlabel2 = data_df['Review Votes']
ylabel3 = data_df['Rating']
plt.ylabel('Rating')
xlabel3 = data_df['Review Votes']
plt.xlabel('Review Votes')
plt.scatter(xlabel3, ylabel3, alpha=0.1)
plt.show() | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False) | 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', 'Review Votes'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
info = info.sort_values(by=('sum', 'Rating'), ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Rating'].sort_values(ascending=False)
corr_matrix = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
temp_data = data_df[0:20000]
temp_data.shape
counter = 0
for i in range(0, 20000):
if abs(temp_data['Rating'][i] - temp_data['SENTIMENT_VALUE'][i]) > 1:
counter += 1
accuracy = (temp_data.shape[0] - counter) / temp_data.shape[0]
testing2 = pd.pivot_table(temp_data, index=['Brand Name'], values=['Rating', 'Review Votes', 'SENTIMENT_VALUE'], columns=[], aggfunc=[np.sum, np.mean], fill_value=0)
testing2 = testing2.sort_values(by=('sum', 'Rating'), ascending=False)
testing2.head(10) | 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 = data_df.corr()
corr_matrix['Price'].sort_values(ascending=False)
data_df = data_df.reset_index(drop=True)
data_df.head() | 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()
koolid2015.sort_values(ascending=False) | 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_name = labelEncoder.fit_transform(train_df.team_1_name)
train_df.team_2_name = labelEncoder.fit_transform(train_df.team_2_name)
train_df.head() | 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.contains('B|M|C')].index
market_cap = df.market_cap.drop(blacklist_index)
def market_cap_conv(price: str):
sym_dict = {'B': 1000000000, 'M': 1000000, 'K': 1000}
price, symbol = price.replace('$', '').split(' ')
price = float(price)
price *= sym_dict[symbol]
return price
market_cap = market_cap.apply(market_cap_conv)
top_market_cap = pd.DataFrame(data={'name': df.name, 'market_cap': market_cap}).sort_values('market_cap', ascending=False)[1:21]
volume_cln_index = df[~df.volume.str.contains('B|M|K', regex=True)].index
df.loc[volume_cln_index, 'volume'] = df.volume[volume_cln_index].str.replace('$', '', regex=False) + 'M'
def volume_cap_conv(price: str):
sym_dict = {'B': 1000000000, 'M': 1000000, 'K': 1000}
price, symbol = price.replace('$', '').split(' ')[:2]
price = float(price)
price *= sym_dict[symbol]
return price
df.volume = df.volume.apply(volume_cap_conv)
top_volume_coins = df.sort_values('volume', ascending=False)[:20]
df[['changes_24h', 'changes_7d', 'changes_30d', 'changes_1y']].apply(lambda col: ~col.str.contains('%')).sum() | code |
89133672/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.head() | code |
89133672/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
TITLE_SIZE = 20
TITLE_PAD = 15
LABELE_SIZE = 15
LABELE_PAD = 10
df = pd.read_csv('../input/top-100-cryptocurrency-2022/Top 100 Cryptocurrency 2022.csv')
df.columns = ['ranking', 'name', 'price', 'changes_24h', 'changes_7d', 'changes_30d', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape
df.isnull().sum()
top_price_alts = df[['name', 'price']][3:23]
sns.set_theme()
fig, ax = plt.subplots(figsize=(11, 9))
sns.barplot(ax=ax, x='price', y='name', data=top_price_alts)
ax.set_title('Top 20 Altcoins by Price', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_xlabel('Price', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
plt.show()
blacklist_index = df[~df.market_cap.str.contains('B|M|C')].index
market_cap = df.market_cap.drop(blacklist_index)
def market_cap_conv(price: str):
sym_dict = {'B': 1000000000, 'M': 1000000, 'K': 1000}
price, symbol = price.replace('$', '').split(' ')
price = float(price)
price *= sym_dict[symbol]
return price
market_cap = market_cap.apply(market_cap_conv)
top_market_cap = pd.DataFrame(data={'name': df.name, 'market_cap': market_cap}).sort_values('market_cap', ascending=False)[1:21]
fig, ax = plt.subplots(figsize=(11, 8))
sns.barplot(ax=ax, x='market_cap', y='name', data=top_market_cap, palette='pastel')
ax.set_title('Top 20 Altcoins by Market Cap', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_xlabel('Market Cap in bn', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.xaxis.set_major_formatter(lambda x, pos: f'{int(x)/1_000_000_000}B')
plt.show()
volume_cln_index = df[~df.volume.str.contains('B|M|K', regex=True)].index
df.loc[volume_cln_index, 'volume'] = df.volume[volume_cln_index].str.replace('$', '', regex=False) + 'M'
def volume_cap_conv(price: str):
sym_dict = {'B': 1000000000, 'M': 1000000, 'K': 1000}
price, symbol = price.replace('$', '').split(' ')[:2]
price = float(price)
price *= sym_dict[symbol]
return price
df.volume = df.volume.apply(volume_cap_conv)
top_volume_coins = df.sort_values('volume', ascending=False)[:20]
fig, ax = plt.subplots(figsize=(11, 8))
palette = sns.color_palette('husl', 6)
sns.barplot(ax=ax, x='volume', y='name', data=top_volume_coins, palette=palette)
ax.set_title('Top 20 Coins by Volume 24H', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_xlabel('Volume', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.xaxis.set_major_formatter(lambda x, pos: f'{float(x) / 1000000000}B')
plt.show() | 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', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape
df.isnull().sum()
top_price_alts = df[['name', 'price']][3:23]
sns.set_theme()
fig, ax = plt.subplots(figsize=(11, 9))
sns.barplot(ax=ax, x='price', y='name', data=top_price_alts)
ax.set_title('Top 20 Altcoins by Price', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_xlabel('Price', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
plt.show() | 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', 'changes_1y', 'market_cap', 'volume', 'supply']
df.shape
df.isnull().sum()
top_price_alts = df[['name', 'price']][3:23]
sns.set_theme()
fig, ax = plt.subplots(figsize=(11, 9))
sns.barplot(ax=ax, x='price', y='name', data=top_price_alts)
ax.set_title('Top 20 Altcoins by Price', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_xlabel('Price', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
plt.show()
blacklist_index = df[~df.market_cap.str.contains('B|M|C')].index
market_cap = df.market_cap.drop(blacklist_index)
def market_cap_conv(price: str):
sym_dict = {'B': 1000000000, 'M': 1000000, 'K': 1000}
price, symbol = price.replace('$', '').split(' ')
price = float(price)
price *= sym_dict[symbol]
return price
market_cap = market_cap.apply(market_cap_conv)
top_market_cap = pd.DataFrame(data={'name': df.name, 'market_cap': market_cap}).sort_values('market_cap', ascending=False)[1:21]
fig, ax = plt.subplots(figsize=(11, 8))
sns.barplot(ax=ax, x='market_cap', y='name', data=top_market_cap, palette='pastel')
ax.set_title('Top 20 Altcoins by Market Cap', fontsize=TITLE_SIZE, pad=TITLE_PAD)
ax.set_xlabel('Market Cap in bn', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.set_ylabel('Crypto Name', fontdict={'fontsize': LABELE_SIZE}, labelpad=LABELE_PAD)
ax.xaxis.set_major_formatter(lambda x, pos: f'{int(x) / 1000000000}B')
plt.show() | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_ecommerce | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(product_data.isnull().sum() / len(product_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum() | 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/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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_top20prod_rev = Df_ecommerce['price'].groupby(Df_ecommerce['product_category_name_english']).sum().sort_values(ascending=False)[:20]
Df_top20prod_rev
fig = plt.figure(figsize=(16, 10))
sns.barplot(y=Df_top20prod_rev.index, x=Df_top20prod_rev.values)
plt.title('Top 20 product category having the largest revenue', fontsize=20)
plt.xlabel('Total revenue', fontsize=17)
plt.ylabel('Product category', fontsize=17) | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(items_data.isnull().sum() / len(items_data) * 100, 2)
items_data.duplicated(['order_id']).sum() | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_top20prod_rev = Df_ecommerce['price'].groupby(Df_ecommerce['product_category_name_english']).sum().sort_values(ascending=False)[:20]
Df_top20prod_rev | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data_2 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
mean_deliver = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_customer_date']).mean()
mean_deliver
added_date = orders_data[orders_data['order_delivered_customer_date'].isnull()]['order_purchase_timestamp'] - mean_deliver
added_date | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum() | 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/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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_top20prod_rev = Df_ecommerce['price'].groupby(Df_ecommerce['product_category_name_english']).sum().sort_values(ascending=False)[:20]
Df_top20prod_rev
fig=plt.figure(figsize=(16,10))
sns.barplot(y=Df_top20prod_rev.index,x=Df_top20prod_rev.values)
plt.title('Top 20 product category having the largest revenue',fontsize=20)
plt.xlabel('Total revenue',fontsize=17)
plt.ylabel('Product category',fontsize=17)
Df_top20prod_rev | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
product_data.dropna(inplace=True)
product_data.isnull().sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(review_data.isnull().sum() / len(review_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
payment_data.duplicated(['order_id']).sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2) | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum() | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data_2 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
mean_deliver = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_customer_date']).mean()
mean_deliver
added_date = orders_data[orders_data['order_delivered_customer_date'].isnull()]['order_purchase_timestamp'] - mean_deliver
added_date
orders_data['order_delivered_customer_date'] = orders_data['order_delivered_customer_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_ecommerce['Expecting_Delivery'] = Df_ecommerce['order_estimated_delivery_date'] - Df_ecommerce['order_purchase_timestamp']
Df_ecommerce['Real_Delivery'] = Df_ecommerce['order_delivered_customer_date'] - Df_ecommerce['order_purchase_timestamp']
Df_ecommerce['Real_Delivery_hour'] = (Df_ecommerce['Real_Delivery'] / np.timedelta64(1, 'h')).round(2)
Df_ecommerce['Expecting_delivery_hour'] = (Df_ecommerce['Expecting_Delivery'] / np.timedelta64(1, 'h')).round(2)
Df_ecommerce['delivery_evaluate'] = round((2 * Df_ecommerce['Expecting_Delivery'] - Df_ecommerce['Real_Delivery']) / Df_ecommerce['Expecting_Delivery'] * 100, 2) | 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/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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
round(payment_data.isnull().sum() / len(payment_data) * 100, 2)
round(review_data.isnull().sum() / len(review_data) * 100, 2)
round(items_data.isnull().sum() / len(items_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2)
round(product_trans_data.isnull().sum() / len(product_trans_data) * 100, 2)
orders_data['order_purchase_timestamp'] = pd.to_datetime(orders_data['order_purchase_timestamp'])
orders_data['order_approved_at'] = pd.to_datetime(orders_data['order_approved_at'])
orders_data['order_delivered_carrier_date'] = pd.to_datetime(orders_data['order_delivered_carrier_date'])
orders_data['order_delivered_customer_date'] = pd.to_datetime(orders_data['order_delivered_customer_date'])
orders_data['order_estimated_delivery_date'] = pd.to_datetime(orders_data['order_estimated_delivery_date'])
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
product_data.dropna(inplace=True)
product_data.isnull().sum()
orders_data.duplicated(['order_id']).sum()
items_data.duplicated(['order_id']).sum()
payment_data.duplicated(['order_id']).sum()
review_data.duplicated(['order_id']).sum()
customers_data.duplicated(['customer_id']).sum()
data_merge = pd.merge(orders_data, items_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, payment_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, review_data, on='order_id', how='inner')
data_merge = pd.merge(data_merge, product_data, on='product_id', how='inner')
data_merge = pd.merge(data_merge, customers_data, on='customer_id', how='inner')
data_merge = pd.merge(data_merge, sellers_data, on='seller_id', how='inner')
data_merge = pd.merge(data_merge, product_trans_data, on='product_category_name', how='inner')
Df_ecommerce = data_merge.drop_duplicates(['order_id'])
Df_ecommerce.isnull().sum()
Df_top20prod_rev = Df_ecommerce['price'].groupby(Df_ecommerce['product_category_name_english']).sum().sort_values(ascending=False)[:20]
Df_top20prod_rev
fig=plt.figure(figsize=(16,10))
sns.barplot(y=Df_top20prod_rev.index,x=Df_top20prod_rev.values)
plt.title('Top 20 product category having the largest revenue',fontsize=20)
plt.xlabel('Total revenue',fontsize=17)
plt.ylabel('Product category',fontsize=17)
Df_top20prod_numsell = Df_ecommerce['order_id'].groupby(Df_ecommerce['product_category_name_english']).count().sort_values(ascending=False)[:20]
fig = plt.figure(figsize=(16, 10))
sns.barplot(y=Df_top20prod_numsell.index, x=Df_top20prod_numsell.values)
plt.title('Top 20 product category having the largest amount of selling', fontsize=20)
plt.xlabel('Number of selling', fontsize=17)
plt.ylabel('Product category', fontsize=17) | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data_2 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
mean_deliver = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_customer_date']).mean()
mean_deliver | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(customers_data.isnull().sum() / len(customers_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(sellers_data.isnull().sum() / len(sellers_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(review_data.isnull().sum() / len(review_data) * 100, 2)
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(customers_data.isnull().sum() / len(customers_data) * 100, 2)
customers_data.duplicated(['customer_id']).sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(geoloca_data.isnull().sum() / len(geoloca_data) * 100, 2) | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.dropna(inplace=True)
orders_data.isnull().sum()
orders_data.duplicated(['order_id']).sum() | 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 = pd.read_csv('../input/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date
orders_data['order_delivered_carrier_date'] = orders_data['order_delivered_carrier_date'].replace(np.nan, added_date)
orders_data.isnull().sum()
orders_data.isnull().sum()
orders_data.isnull().sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(review_data.isnull().sum() / len(review_data) * 100, 2)
review_data.isnull().sum()
review_data.drop(columns=['review_comment_title', 'review_comment_message'], inplace=True)
review_data.isnull().sum()
review_data.duplicated(['order_id']).sum() | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(orders_data.isnull().sum() / len(orders_data) * 100, 2)
orders_data.order_purchase_timestamp
orders_data_1 = orders_data[orders_data['order_delivered_carrier_date'].notnull()]
miss_carrier = (orders_data_1['order_purchase_timestamp'] - orders_data_1['order_delivered_carrier_date']).mean()
miss_carrier
added_date = orders_data[orders_data['order_delivered_carrier_date'].isnull()]['order_purchase_timestamp'] - miss_carrier
added_date | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(product_data.isnull().sum() / len(product_data) * 100, 2)
round(product_data.isnull().sum() / len(product_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(payment_data.isnull().sum() / len(payment_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(items_data.isnull().sum() / len(items_data) * 100, 2) | 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/brazilian-ecommerce/olist_order_items_dataset.csv')
product_data = pd.read_csv('../input/brazilian-ecommerce/olist_products_dataset.csv')
customers_data = pd.read_csv('../input/brazilian-ecommerce/olist_customers_dataset.csv')
sellers_data = pd.read_csv('../input/brazilian-ecommerce/olist_sellers_dataset.csv')
product_trans_data = pd.read_csv('../input/brazilian-ecommerce/product_category_name_translation.csv')
geoloca_data = pd.read_csv('../input/brazilian-ecommerce/olist_geolocation_dataset.csv')
round(review_data.isnull().sum() / len(review_data) * 100, 2)
review_data.isnull().sum() | 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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