path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18147692/cell_45 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset... | code |
18147692/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.head() | code |
18147692/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
plt.bar(data_age1.age, data_age1.tenure)
plt... | code |
18147692/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.head() | code |
18147692/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
data_age1[data_age1['tenure'] == data_age1['... | code |
18147692/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
data_age1.head() | code |
18147692/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
105203552/cell_21 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Sex', 'Survived']].groupby... | code |
105203552/cell_9 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.info() | code |
105203552/cell_23 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Parch', 'Survived']].group... | code |
105203552/cell_30 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_33 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_44 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-... | code |
105203552/cell_20 | [
"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('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Pclass', 'Survived']].grou... | code |
105203552/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.tail() | code |
105203552/cell_39 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_26 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_41 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/... | code |
105203552/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.describe() | code |
105203552/cell_45 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-... | code |
105203552/cell_18 | [
"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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.fi... | code |
105203552/cell_32 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_15 | [
"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('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
category2 = ['Cabin', 'Name', 'Ticket... | code |
105203552/cell_38 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_35 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_43 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_14 | [
"text_plain_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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.fi... | code |
105203552/cell_22 | [
"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('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['SibSp', 'Survived']].group... | code |
105203552/cell_37 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
105203552/cell_5 | [
"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('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns | code |
105203552/cell_36 | [
"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_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_sub... | code |
106199562/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
headers = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'st... | code |
106199562/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
headers = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-... | code |
16153941/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.... | code |
16153941/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.... | code |
16153941/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum() | code |
16153941/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.head(2) | code |
16153941/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.... | code |
16153941/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes | code |
16153941/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes
data = data.drop(['instant', 'dteday'], axis=1)... | code |
16153941/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.head(2) | code |
32067376/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape | code |
32067376/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.head() | code |
32067376/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado | code |
32067376/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum() | code |
32067376/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/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 |
32067376/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index | code |
32067376/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns | code |
32067376/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.me... | code |
32067376/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.tail(3) | code |
72104176/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-dam... | code |
72104176/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthq... | code |
72104176/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthq... | code |
72104176/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
print(train_vals.head())
... | code |
72104176/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.info() | code |
72104176/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthq... | code |
72104176/cell_19 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-dam... | code |
72104176/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 |
72104176/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_... | code |
72104176/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
test_vals = pd.read_csv(... | code |
72104176/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_... | code |
72104176/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-dam... | code |
72104176/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-dam... | code |
72104176/cell_3 | [
"image_output_1.png"
] | import matplotlib
import matplotlib
for cname in matplotlib.colors.cnames:
print(cname) | code |
72104176/cell_17 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv... | code |
72104176/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_... | code |
72104176/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.head() | code |
128034496/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
print('Lima data teratas:')
print(df.head())
print('\nInfo dataset:')
print(df.info()) | code |
128034496/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transa... | code |
128034496/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'... | code |
128034496/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transa... | code |
128034496/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'... | code |
128034496/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', ori... | code |
128034496/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transa... | code |
128034496/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', ori... | code |
130012258/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attac... | code |
130012258/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df | code |
130012258/cell_23 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attac... | code |
130012258/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df | code |
130012258/cell_19 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Conten... | code |
130012258/cell_18 | [
"text_html_output_1.png"
] | from nltk.stem.porter import PorterStemmer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)... | code |
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