path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
104120795/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
print(df.shape) | code |
104120795/cell_23 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.info() | code |
104120795/cell_20 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
corr = df[features].corr()
plt.figure(figsize=(16, 16))
sns.heatmap(corr, cbar=True, square=True, annot=True, fmt='.2f', annot_kws={'size': 15}, xticklabels=features, yticklabels=features, alpha=0.7, cmap='coolwarm')
plt.show() | code |
104120795/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes | code |
104120795/cell_18 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
plt.figure(figsize=(8, 8))
sns.pairplot(df[features], palette='coolwarm')
plt.show() | code |
104120795/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
df['Type'].value_counts() | code |
104120795/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
df.describe() | code |
104120795/cell_16 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
df[features].plot(figsize=(8, 6), kind='box') | code |
104120795/cell_38 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
features_boxcox = []
for feature in features:
bc_transformed, _ = boxcox(df[feature] + 1)
features_boxcox.append(bc_transformed)
features_boxcox = np.column_stack(features_boxcox)
df_bc = pd.DataFrame(data=features_boxcox, columns=features)
df_bc['Type'] = df['Type']
for feature in features:
delta = np.abs(df_bc[feature].skew() / df[feature].skew())
if delta < 1.0:
print('Feature %s is less skewed after a Box-Cox transform' % feature)
else:
print('Feature %s is more skewed after a Box-Cox transform' % feature) | code |
104120795/cell_31 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
corr = df[features].corr()
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
for feat in features:
skew = df[feat].skew()
sns.countplot(df['Type'])
plt.show() | code |
104120795/cell_14 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
print('The dataset contains %d observations with more than 2 outliers' % len(outlier_hunt(df[features]))) | code |
104120795/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
corr = df[features].corr()
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
for feat in features:
skew = df[feat].skew()
sns.distplot(df[feat], kde=False, label='Skew = %.3f' % skew, bins=30)
plt.legend(loc='best')
plt.show() | code |
104120795/cell_37 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
corr = df[features].corr()
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
for feat in features:
skew = df[feat].skew()
features_boxcox = []
for feature in features:
bc_transformed, _ = boxcox(df[feature] + 1)
features_boxcox.append(bc_transformed)
features_boxcox = np.column_stack(features_boxcox)
df_bc = pd.DataFrame(data=features_boxcox, columns=features)
df_bc['Type'] = df['Type']
for feature in features:
fig, ax = plt.subplots(1, 2, figsize=(7, 3.5))
ax[0].hist(df[feature], color='blue', bins=30, alpha=0.3, label='Skew = %s' % str(round(df[feature].skew(), 3)))
ax[0].set_title(str(feature))
ax[0].legend(loc=0)
ax[1].hist(df_bc[feature], color='red', bins=30, alpha=0.3, label='Skew = %s' % str(round(df_bc[feature].skew(), 3)))
ax[1].set_title(str(feature) + ' after a Box-Cox transformation')
ax[1].legend(loc=0)
plt.show() | code |
104120795/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # visualization
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
sns.distplot(df[feat], kde=False, label='Skew = %.3f' % skew, bins=30)
plt.legend(loc='best')
plt.show() | code |
104120795/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.head(15) | code |
104120795/cell_36 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
features_boxcox = []
for feature in features:
bc_transformed, _ = boxcox(df[feature] + 1)
features_boxcox.append(bc_transformed)
features_boxcox = np.column_stack(features_boxcox)
df_bc = pd.DataFrame(data=features_boxcox, columns=features)
df_bc['Type'] = df['Type']
df_bc.describe() | code |
2044446/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import datetime
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.axis('off')
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets']
def getday(x):
year, month, day = (int(i) for i in x.split('-'))
answer = datetime.date(year, month, day).weekday()
return day_order[answer]
df['day'] = df['date'].apply(getday)
day_df = pd.DataFrame(df['day'].value_counts()).reset_index()
day_df.columns = ['day', 'tweets']
mesting = df2[df2['username'] == 'MESTAfrica']
month_mest = pd.DataFrame(mesting['month'].value_counts()).reset_index()
month_mest.columns = ['month', 'tweets']
month_mest.head() | code |
2044446/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
plt.figure(figsize=(12, 8))
sns.countplot(data=df, y='username') | code |
2044446/cell_25 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.figure(figsize=(12, 15))
plt.imshow(wordcloud)
plt.axis('off')
plt.show() | code |
2044446/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.head() | code |
2044446/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets'] | code |
2044446/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.figure(figsize=(12, 15))
plt.imshow(wordcloud)
plt.axis('off')
plt.show() | code |
2044446/cell_33 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2.head() | code |
2044446/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import datetime
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.axis('off')
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets']
def getday(x):
year, month, day = (int(i) for i in x.split('-'))
answer = datetime.date(year, month, day).weekday()
return day_order[answer]
df['day'] = df['date'].apply(getday)
day_df = pd.DataFrame(df['day'].value_counts()).reset_index()
day_df.columns = ['day', 'tweets']
mesting = df2[df2['username'] == 'MESTAfrica']
month_mest = pd.DataFrame(mesting['month'].value_counts()).reset_index()
month_mest.columns = ['month', 'tweets']
month_mest['tweets'].sum() | code |
2044446/cell_20 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.figure(figsize=(12, 15))
plt.imshow(wordcloud)
plt.axis('off')
plt.show() | code |
2044446/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any() | code |
2044446/cell_29 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
mest[mest['retweets'] == 2157] | code |
2044446/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import datetime
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.axis('off')
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets']
def getday(x):
year, month, day = (int(i) for i in x.split('-'))
answer = datetime.date(year, month, day).weekday()
return day_order[answer]
df['day'] = df['date'].apply(getday)
day_df = pd.DataFrame(df['day'].value_counts()).reset_index()
day_df.columns = ['day', 'tweets']
mesting = df2[df2['username'] == 'MESTAfrica']
month_mest = pd.DataFrame(mesting['month'].value_counts()).reset_index()
month_mest.columns = ['month', 'tweets']
plt.figure(figsize=(12, 6))
plt.title('MESTAfrica Tweets Per Month')
sns.barplot(x='month', y='tweets', data=month_mest, order=month_order) | code |
2044446/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
import datetime
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2044446/cell_32 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
df2.head() | code |
2044446/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.describe() | code |
2044446/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10] | code |
2044446/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10] | code |
2044446/cell_35 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.axis('off')
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets']
plt.figure(figsize=(12, 6))
plt.title('All Tweets Per Day')
sns.barplot(x='month', y='tweets', data=month_df, order=month_order) | code |
2044446/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df[df['retweets'] == 79537] | code |
2044446/cell_27 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
mest.describe() | code |
2044446/cell_37 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import datetime
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any()
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort_values('retweets', ascending=False)[:10]
corpus = ' '.join(df['tweet '])
corpus = corpus.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpus)
plt.axis('off')
mest = df[df['username'] == 'MESTAfrica']
corpu = ' '.join(df['tweet '])
corpu = corpu.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corpu)
plt.axis('off')
tony = df[df['username'] == 'TonyElumeluFDN']
corp = ' '.join(df['tweet '])
corp = corp.replace('.', '. ')
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=2400, height=2000).generate(corp)
plt.axis('off')
df2 = df
df2['date'] = df2['created_at'].map(lambda x: x.split(' ')[0])
df2['time'] = df2['created_at'].map(lambda x: x.split(' ')[-1])
del df2['created_at']
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
df2 = df[['tweet_id', 'date', 'time', 'tweet ', 'retweets', 'username']]
df2['month'] = df2['date'].apply(lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(df2['month'].value_counts()).reset_index()
month_df.columns = ['month', 'tweets']
def getday(x):
year, month, day = (int(i) for i in x.split('-'))
answer = datetime.date(year, month, day).weekday()
return day_order[answer]
df['day'] = df['date'].apply(getday)
day_df = pd.DataFrame(df['day'].value_counts()).reset_index()
day_df.columns = ['day', 'tweets']
plt.figure(figsize=(12, 6))
plt.title('All Tweets Per Day')
sns.barplot(x='day', y='tweets', data=day_df, order=day_order) | code |
2044446/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.info() | code |
88075343/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
results = df1.groupby('Month').sum()['sales']
import matplotlib.pyplot as plt
months = range(1, 13)
plt.bar(months, results)
plt.xticks(months)
plt.ylabel('Sales')
plt.xlabel('Month Number')
plt.ticklabel_format(style='plain')
plt.show() | code |
88075343/cell_30 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
results = df1.groupby('Month').sum()['sales']
def getCity(str):
return str.split(',')[1]
def getState(str):
return str.split(',')[2].split(' ')[1]
df1['Purchase Address'].apply(lambda x: x.split(',')[2].split(' ')[1])
df1['city'] = df1['Purchase Address'].apply(lambda x: f'{getCity(x)} ({getState(x)})')
results = df1.groupby('city').sum()
results | code |
88075343/cell_6 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
df1.head() | code |
88075343/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
df.head() | code |
88075343/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
df1.head() | code |
88075343/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88075343/cell_7 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1.head() | code |
88075343/cell_28 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
results = df1.groupby('Month').sum()['sales']
def getCity(str):
return str.split(',')[1]
def getState(str):
return str.split(',')[2].split(' ')[1]
df1['Purchase Address'].apply(lambda x: x.split(',')[2].split(' ')[1])
df1['city'] = df1['Purchase Address'].apply(lambda x: f'{getCity(x)} ({getState(x)})')
df1.head() | code |
88075343/cell_15 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1.head() | code |
88075343/cell_24 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
results = df1.groupby('Month').sum()['sales']
df1.head() | code |
88075343/cell_27 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape
nan_df = df1[df1.isna().any(axis=1)]
df1 = df1.dropna(how='all')
df1 = df1[df1['Order Date'].str[0:2] != 'Or']
df1['sales'] = 0
df1['sales'] = df1['Price Each'].astype('float') * df1['Quantity Ordered'].astype('float')
results = df1.groupby('Month').sum()['sales']
df1['Purchase Address'].apply(lambda x: x.split(',')[2].split(' ')[1]) | code |
88075343/cell_5 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv('../input/sales-dataset/Sales_April_2019.csv')
files = [file for file in os.listdir('../input/sales-dataset')]
df = pd.DataFrame()
for file in files:
df1 = pd.read_csv('../input/sales-dataset/' + file)
df = pd.concat([df, df1])
df.to_csv('all_data.csv', index=False)
df1 = pd.read_csv('all_data.csv')
df1.shape | code |
128022780/cell_13 | [
"text_plain_output_1.png"
] | x = 4
x = 2
y = 902385873792631
z = -4938686
x = 2.1
y = 2.0
z = -45.69
x = 3 + 4j
y = 4j
z = -4j
print(type(x))
print(type(y)) | code |
128022780/cell_4 | [
"text_plain_output_1.png"
] | x = 4
print(type(x)) | code |
128022780/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | x = 4
x = 2
y = 902385873792631
z = -4938686
print(type(x))
print(type(y))
print(type(z)) | code |
128022780/cell_18 | [
"text_plain_output_1.png"
] | import random
import random
print(random.randrange(1, 1)) | code |
128022780/cell_16 | [
"text_plain_output_1.png"
] | x = 4
x = 2
y = 902385873792631
z = -4938686
x = 2.1
y = 2.0
z = -45.69
x = 3 + 4j
y = 4j
z = -4j
x = 1
y = 4.4
z = 1j
a = float(x)
b = int(y)
c = complex(x)
print(a)
print(b)
print(c)
print(type(a))
print(type(b))
print(type(c)) | code |
128022780/cell_10 | [
"text_plain_output_1.png"
] | x = 4
x = 2
y = 902385873792631
z = -4938686
x = 2.1
y = 2.0
z = -45.69
print(type(x))
print(type(y))
print(type(x)) | code |
16124219/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
df_.head() | code |
16124219/cell_34 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
df_.head() | code |
16124219/cell_40 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
y.min()
y[0:5] | code |
16124219/cell_65 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
models = []
models.append(('Linear', LinearRegression()))
models.append(('GBooting', GradientBoostingRegressor()))
models.append(('RFR', RandomForestRegressor(n_estimators=10)))
models.append(('DTR', DecisionTreeRegressor()))
def rmsle(y_pred, y_test):
assert len(ytest) == len(ypred)
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_test)) ** 2))
rmse_calc = []
rmsle = []
for nome, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse_calc.append((nome, np.sqrt(mean_squared_error(y_test, y_pred))))
rmsle.append((nome, np.sqrt(mean_squared_log_error(y_test, y_pred))))
rmsle | code |
16124219/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
df_.head() | code |
16124219/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
models = []
models.append(('Linear', LinearRegression()))
models.append(('GBooting', GradientBoostingRegressor()))
models.append(('RFR', RandomForestRegressor(n_estimators=10)))
models.append(('DTR', DecisionTreeRegressor()))
def rmsle(y_pred, y_test):
assert len(ytest) == len(ypred)
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_test)) ** 2))
rmse_calc = []
rmsle = []
for nome, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse_calc.append((nome, np.sqrt(mean_squared_error(y_test, y_pred))))
rmsle.append((nome, np.sqrt(mean_squared_log_error(y_test, y_pred))))
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
watchlist = [(dtrain, 'train'), (dtest, 'valid')]
xgb_pars = {'min_child_weight': 10, 'eta': 0.03, 'colsample_bytree': 0.3, 'max_depth': 10, 'subsample': 0.8, 'lambda': 0.5, 'nthread': -1, 'booster': 'gbtree', 'silent': 1, 'eval_metric': 'rmse', 'objective': 'reg:linear'}
model = xgb.train(xgb_pars, dtrain, 1000, watchlist, early_stopping_rounds=90, maximize=False, verbose_eval=100)
y_pred = model.predict(dtest)
y_pred | code |
16124219/cell_64 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
models = []
models.append(('Linear', LinearRegression()))
models.append(('GBooting', GradientBoostingRegressor()))
models.append(('RFR', RandomForestRegressor(n_estimators=10)))
models.append(('DTR', DecisionTreeRegressor()))
def rmsle(y_pred, y_test):
assert len(ytest) == len(ypred)
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_test)) ** 2))
rmse_calc = []
rmsle = []
for nome, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse_calc.append((nome, np.sqrt(mean_squared_error(y_test, y_pred))))
rmsle.append((nome, np.sqrt(mean_squared_log_error(y_test, y_pred))))
rmse_calc | code |
16124219/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
16124219/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
corr = df_.corr()
sns.heatmap(corr)
plt.show() | code |
16124219/cell_58 | [
"text_plain_output_1.png"
] | import xgboost as xgb
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
watchlist = [(dtrain, 'train'), (dtest, 'valid')]
xgb_pars = {'min_child_weight': 10, 'eta': 0.03, 'colsample_bytree': 0.3, 'max_depth': 10, 'subsample': 0.8, 'lambda': 0.5, 'nthread': -1, 'booster': 'gbtree', 'silent': 1, 'eval_metric': 'rmse', 'objective': 'reg:linear'}
model = xgb.train(xgb_pars, dtrain, 1000, watchlist, early_stopping_rounds=90, maximize=False, verbose_eval=100) | code |
16124219/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
print(df.shape) | code |
16124219/cell_47 | [
"image_output_1.png"
] | X_train | code |
16124219/cell_66 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
models = []
models.append(('Linear', LinearRegression()))
models.append(('GBooting', GradientBoostingRegressor()))
models.append(('RFR', RandomForestRegressor(n_estimators=10)))
models.append(('DTR', DecisionTreeRegressor()))
def rmsle(y_pred, y_test):
assert len(ytest) == len(ypred)
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_test)) ** 2))
rmse_calc = []
rmsle = []
for nome, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse_calc.append((nome, np.sqrt(mean_squared_error(y_test, y_pred))))
rmsle.append((nome, np.sqrt(mean_squared_log_error(y_test, y_pred))))
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
watchlist = [(dtrain, 'train'), (dtest, 'valid')]
xgb_pars = {'min_child_weight': 10, 'eta': 0.03, 'colsample_bytree': 0.3, 'max_depth': 10, 'subsample': 0.8, 'lambda': 0.5, 'nthread': -1, 'booster': 'gbtree', 'silent': 1, 'eval_metric': 'rmse', 'objective': 'reg:linear'}
model = xgb.train(xgb_pars, dtrain, 1000, watchlist, early_stopping_rounds=90, maximize=False, verbose_eval=100)
y_pred = model.predict(dtest)
rmse_xgb = np.sqrt(mean_squared_error(y_test, y_pred))
rmsle_xgb = np.sqrt(mean_squared_log_error(y_test, y_pred))
rmsle_xgb | code |
16124219/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
std = StandardScaler()
X_train_str = std.fit_transform(X_train)
X_test_str = std.transform(X_test)
df_std = std.fit_transform(df_)
df_std = pd.DataFrame(df_std)
df_std.head() | code |
16124219/cell_46 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
corr = df_.corr()
corr | code |
16124219/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
for c in cols:
plt.figure()
plt.title(c)
time[c].plot(kind='hist')
plt.show() | code |
16124219/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
df_['passenger_count'].unique() | code |
16124219/cell_53 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
models = []
models.append(('Linear', LinearRegression()))
models.append(('GBooting', GradientBoostingRegressor()))
models.append(('RFR', RandomForestRegressor(n_estimators=10)))
models.append(('DTR', DecisionTreeRegressor()))
def rmsle(y_pred, y_test):
assert len(ytest) == len(ypred)
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_test)) ** 2))
rmse_calc = []
rmsle = []
for nome, model in models:
print(nome)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse_calc.append((nome, np.sqrt(mean_squared_error(y_test, y_pred))))
rmsle.append((nome, np.sqrt(mean_squared_log_error(y_test, y_pred)))) | code |
16124219/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
df_.head() | code |
16124219/cell_37 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_ = df.drop(['id', 'vendor_id', 'store_and_fwd_flag'], axis=1)
time = df_.set_index('trip_duration')
cols = ['passenger_count', 'Turno', 'Dia', 'Mês', 'Dia_Semana']
df_.drop(['dropoff_datetime', 'pickup_datetime', 'Hora', 'Data'], axis=1, inplace=True)
def distancia(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
R_terra = 6371
inicio_lat, inicio_lon, fim_lat, fim_lon = map(np.radians, [pickup_lat, pickup_lon, dropoff_lat, dropoff_lon])
dlat = fim_lat - inicio_lat
dlon = fim_lon - inicio_lon
d = np.sin(dlat / 2.0) ** 2 + np.cos(inicio_lat) * np.cos(fim_lat) * np.sin(dlon / 2.0) ** 2
return 2 * R_terra * np.arcsin(np.sqrt(d))
df_['Duração'] = df_['trip_duration']
df_.drop(['trip_duration'], axis=1, inplace=True)
X = df_.values[:, :-1]
y = np.log(df_.values[:, -1] + 1)
y.min() | code |
16111538/cell_9 | [
"text_html_output_1.png"
] | 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')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
train['SalePrice'].hist(bins=50)
y = train['SalePrice'].reset_index(drop=True) | code |
16111538/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
16111538/cell_7 | [
"image_output_1.png"
] | 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')
train['SalePrice'].hist(bins=50) | code |
16111538/cell_10 | [
"text_html_output_1.png"
] | 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')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(drop=True)
train = train.drop(['Id', 'SalePrice'], axis=1)
test = test.drop(['Id'], axis=1)
x = pd.concat([train, test]).reset_index(drop=True)
x.describe() | code |
16111538/cell_5 | [
"text_html_output_1.png"
] | 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')
train.describe() | code |
121150515/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
dt = DecisionTreeRegressor()
cross_val_score(dt, X, y, cv=5).mean() | code |
121150515/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv', index_col='id')
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True, cmap='BrBG') | code |
121150515/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(max_depth=6, random_state=73, n_estimators=90)
print(cross_val_score(rf, X, y, cv=5).mean()) | code |
121150515/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import RidgeCV
from sklearn.linear_model import RidgeCV
ridge = RidgeCV(cv=5).fit(X, y)
ridge.score(X, y) | code |
121150515/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LassoCV
from sklearn.linear_model import LassoCV
lasso = LassoCV(cv=5).fit(X, y)
lasso.score(X, y) | code |
121150515/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
cross_val_score(lr, X, y, cv=5).mean() | code |
121150515/cell_10 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor(n_neighbors=9)
cross_val_score(knn, X, y, cv=5).mean() | code |
121150515/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split,cross_val_score
import xgboost as xgb
model = xgb.XGBRegressor(max_depth=5, n_estimators=10, random_state=73)
print(cross_val_score(model, X, y, cv=5).mean()) | code |
32065347/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
spotify_filepath = '../input/data-for-datavis/spotify.csv'
spotify_data = pd.read_csv(spotify_filepath, index_col='Date', parse_dates=True)
spotify_data.sample(10) | code |
32065347/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32065347/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sns.set_style('dark')
spotify_filepath = '../input/data-for-datavis/spotify.csv'
spotify_data = pd.read_csv(spotify_filepath, index_col='Date', parse_dates=True)
spotify_data.sample(10)
plt.figure(figsize=(12, 6))
sns.lineplot(data=spotify_data) | code |
32065347/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sns.set_style('dark')
spotify_filepath = '../input/data-for-datavis/spotify.csv'
spotify_data = pd.read_csv(spotify_filepath, index_col='Date', parse_dates=True)
spotify_data.sample(10)
ign_filepath = '../input/data-for-datavis/ign_scores.csv'
ign_data = pd.read_csv(ign_filepath, index_col='Platform')
plt.figure(figsize=(8, 6))
sns.barplot(x=ign_data['Racing'], y=ign_data.index)
plt.xlabel('')
plt.title('Average Score for Racing Games, by Platform') | code |
32065347/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sns.set_style('dark')
spotify_filepath = '../input/data-for-datavis/spotify.csv'
spotify_data = pd.read_csv(spotify_filepath, index_col='Date', parse_dates=True)
spotify_data.sample(10)
ign_filepath = '../input/data-for-datavis/ign_scores.csv'
ign_data = pd.read_csv(ign_filepath, index_col='Platform')
plt.figure(figsize=(8, 6))
sns.heatmap(data=ign_data, annot=True) | code |
89142938/cell_3 | [
"text_plain_output_1.png"
] | def factorial(n):
result = 1
for i in range(1, n + 1):
result = result * i
return result
factorial(5) | code |
105180553/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.colab import drive
from google.colab import drive
drive.mount('/content/drive') | code |
128044935/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('./train.csv')
train_data.head() | code |
128044935/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128049433/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary() | code |
128049433/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names | code |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.