path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90108519/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.l... | code |
90108519/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_ma... | code |
90108519/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_ma... | code |
90108519/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
df.info() | code |
90108519/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.head() | code |
90108519/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.l... | code |
90108519/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask] | code |
90108519/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.l... | code |
16119977/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.windspeed.plot(kind='box') | code |
16119977/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum() | code |
16119977/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.info() | code |
16119977/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)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum() | code |
16119977/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['temp'].unique() | code |
16119977/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16119977/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape | code |
16119977/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.registered.plot(kind='box') | code |
16119977/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].plot(kind='box') | code |
16119977/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.head() | code |
16119977/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].value_counts() | code |
16119977/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.casual.plot(kind='box') | code |
16119977/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum() | code |
16119977/cell_12 | [
"text_html_output_1.png"
] | dataf.season.plot(kind='box') | code |
16119977/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T | code |
2016288/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.r... | code |
2016288/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
print(fashion_train.isnull().sum().sum())
print(fashion_test.isnull().sum().sum()) | code |
2016288/cell_11 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.r... | code |
2016288/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2016288/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
fashion_train.head() | code |
2016288/cell_10 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.r... | code |
2016288/cell_5 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
from keras.utils import to_... | code |
326868/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model impo... | code |
326868/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326868/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model impo... | code |
326868/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mo... | code |
326868/cell_18 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.en... | code |
326868/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegressio... | code |
326868/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.en... | code |
326868/cell_24 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326868/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Coun... | code |
326868/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326868/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassif... | code |
326868/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.en... | code |
50227272/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
de... | code |
50227272/cell_25 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
de... | code |
50227272/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
de... | code |
50227272/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df... | code |
50227272/cell_14 | [
"text_html_output_2.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df... | code |
18135360/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult.head() | code |
18135360/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes | code |
18135360/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape | code |
18135360/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN']) | code |
18135360/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.head() | code |
18135360/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns | code |
18135360/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1) | code |
18135360/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.head() | code |
18135360/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': ... | code |
18135360/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0) | code |
18135360/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum() | code |
18135360/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.head() | code |
34123573/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
y_k | code |
34123573/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual... | code |
34123573/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import plotly
import plotly.express as px
import cufflinks as cf
import plotly.offline as pyo
from plotly.offline import init_notebook_mode, plot, iplot
pyo.init_notebook_mode(connected=True)
cf.go_offline() | code |
34123573/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_... | code |
34123573/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k | code |
34123573/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
print(df.head()) | code |
34123573/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_... | code |
34123573/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)
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.describe() | code |
34123573/cell_15 | [
"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
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)... | code |
34123573/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34123573/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_... | code |
34123573/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual... | code |
34123573/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)... | code |
34123573/cell_22 | [
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spen... | code |
34123573/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
sns.heatmap(df.drop(['CustomerID'], axis=1).corr(), annot=True) | code |
34123573/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual... | code |
88104888/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data_path = '../input/nuclio10-dsc-1121/sales_train_merged.csv'
df = pd.read_csv(data_path, index_col=0)
df.head() | code |
72115608/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col i... | code |
72115608/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
72115608/cell_23 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_20 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_29 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col i... | code |
72115608/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
72115608/cell_18 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_15 | [
"text_html_output_1.png"
] | from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation... | code |
72115608/cell_16 | [
"text_html_output_1.png"
] | from keras.layers import Dense
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation=... | code |
72115608/cell_14 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation... | code |
72115608/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
72115608/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.info() | code |
105197097/cell_9 | [
"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/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.head() | code |
105197097/cell_23 | [
"text_plain_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_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.c... | code |
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