path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17118879/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pathlib
import random
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.... | code |
17118879/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df.head(4) | code |
17118879/cell_15 | [
"text_plain_output_1.png"
] | import pathlib
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0] | code |
17118879/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.getcwd()
os.listdir() | code |
17118879/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214']
cla... | code |
17118879/cell_14 | [
"text_html_output_1.png"
] | import pathlib
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
print(item)
break | code |
17118879/cell_22 | [
"text_plain_output_1.png"
] | import pathlib
import random
import tensorflow as tf
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0]
all_paths = [str(path) for path i... | code |
17118879/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214'] | code |
17118879/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
test_df.info() | code |
128029150/cell_21 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kag... | code |
128029150/cell_13 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' +... | code |
128029150/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_me... | code |
128029150/cell_25 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_m... | code |
128029150/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'") | code |
128029150/cell_20 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/... | code |
128029150/cell_26 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_met... | code |
128029150/cell_11 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewTe... | code |
128029150/cell_19 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_met... | code |
128029150/cell_18 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kag... | code |
128029150/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'")
data_meta_1 = data_me... | code |
128029150/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_m... | code |
128029150/cell_16 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_met... | code |
128029150/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta | code |
128029150/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas import DataFrame
from sklearn import neighbors
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/... | code |
128029150/cell_10 | [
"text_html_output_1.png"
] | from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import pandas as pd
import re
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewTe... | code |
128029150/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_meta = pd.read_csv('/kaggle/input/content-based/meta_full_csv')
data_meta = data_meta.sample(50000)
data_meta['summaryRev'] = data_meta['summary'] + ' ' + data_meta['reviewText']
data_meta.query("asin == 'B00GIDADP0'")
data_meta.query("asin == 'B002Q46RDW'") | code |
16135360/cell_42 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', '... | code |
16135360/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_25 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale'... | code |
16135360/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', '... | code |
16135360/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', '... | code |
16135360/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T | code |
16135360/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale'... | code |
16135360/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16135360/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_28 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_43 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', '... | code |
16135360/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_24 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_22 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16135360/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cities_r2.csv')
df.head().T
df.columns = ['city', 'scode', 'state', 'dcode', 'poptotal', 'popmale', 'popfemale', '0-6poptotal', '0-6popmale', '0-6popfemale', 'litertotal', 'litermale', 'literfemale', 'sexratio', '0-6sexratio', 'elrtotal', 'elrmale', 'elrfemale', 'locatio... | code |
16166088/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = str(data['cuisines'][i]).split(', ... | code |
16166088/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = s... | code |
16166088/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
_data.head() | code |
16166088/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16166088/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
print('Original set of columns:{}'.format(_data.columns))
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
print('New columns : {}'.format(columns)) | code |
16166088/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
cuisine = []
for i in range(0, data['cuisines'].count()):
splist = str(data['cuisines'][i]).split(', ... | code |
105212943/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop(... | code |
105212943/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score']
df.groupby('churn')[featur... | code |
105212943/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape | code |
105212943/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import missingno as msno
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,LabelEncoder
import scipy.special
import scipy.stats as stats
from scipy.stats import skew, kurtosis, shapiro
!... | code |
105212943/cell_11 | [
"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 plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop(... | code |
105212943/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 |
105212943/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe | code |
105212943/cell_18 | [
"text_html_output_2.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.rea... | code |
105212943/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes | code |
105212943/cell_17 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
... | code |
105212943/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop(... | code |
105212943/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop('churn')
subsets = ['credit_score... | code |
105212943/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.shape
df.describe
df.dtypes
features = df.keys()
features = features.drop(... | code |
105212943/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/bank-customer-churn-dataset/Bank Customer Churn Prediction.csv'
df = pd.read_csv(data)
df.head() | code |
49129186/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 |
49129186/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', header=1, na_values='NaN')
data = data.fillna('Not Given')
data.head() | code |
49129186/cell_12 | [
"text_html_output_1.png"
] | from IPython.display import display,clear_output
from ipywidgets import interact, interactive, fixed, interact_manual,VBox,HBox,Layout
import ipywidgets as widgets
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
data = pd.read_csv('/kagg... | code |
18153034/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
games = pd.read_csv('../input/games.csv')
games.head(2) | code |
18153034/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
18153034/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
sns.set(color_codes=True, style='darkgrid')
games = pd.read_csv('../input/games.csv')
games = games[games.rated]
games['mean_rating'] = (games.white_rating +... | code |
122262213/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import Dec... | code |
122262213/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
df.columns
df.head() | code |
122262213/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train) | code |
122262213/cell_7 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
from sklearn.tree import Dec... | code |
122262213/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text
import pandas as pd
df = pd... | code |
122262213/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
df.columns | code |
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