path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106198328/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.info() | code |
106198328/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum()
perc = 1
min_count = int((100 - perc) / 100 * stock_data.shape[0] + 1)
stock_data = stock_data.dropn... | code |
106198328/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum()
perc = 1
min_count = int((100 - perc) / 100 * stock_data.shape[0] + 1)
stock_data = stock_data.dropn... | code |
106198328/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2) | code |
106198328/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum() | code |
106198328/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data | code |
130014120/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
print('Full train dataset shape is {}'.format(ds_df.shape))
print('Dataset head:')
ds_df.head(10) | code |
130014120/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0] | code |
130014120/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0]
sns.kdeplot(data=ds_df['SalePrice']) | code |
130014120/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0]
sns.kdeplot(np.log10(ds_df['SalePrice'])) | code |
130014120/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import math
train_file_path = '../input/house-prices-advanced-regression-techniques/train.csv'
test_file_path = '../input/house-prices-advanced-regression-techniques/test.csv'
print('Done') | code |
130014120/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
ds_df['SalePrice'].isna().sum() | code |
122249621/cell_9 | [
"text_plain_output_1.png"
] | ! pip install google-colab | code |
122249621/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train = df_train.rename(columns={'Image Index': 'filename', 'Bbox [x': 'x', 'h]': 'h', 'Finding Label': 'class'})
df_train = df_train.drop(['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'], axis=1)
df_train.head() | code |
122249621/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train.head() | code |
122249621/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train = df_train.rename(columns={'Image Index': 'filename', 'Bbox [x': 'x', 'h]': 'h', 'Finding Label': 'class'})
df_train = df_train.drop(['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'], axis=1)
df_train['bbox'] = df_train[['x', 'y',... | code |
122249621/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
print(f"количество изображений {df_train['Image Index'].nunique()}")
df_train.head(1) | code |
106192159/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test | code |
106192159/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_30 | [
"text_html_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() ... | code |
106192159/cell_33 | [
"image_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() ... | code |
106192159/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_train.isnull().sum()
df_train.isnull().mean() * 100 | code |
106192159/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() *... | code |
106192159/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_1 | [
"text_plain_output_1.png"
] | !pip install feature_engine | code |
106192159/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_test.isnull().mean() * 100 | code |
106192159/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_32 | [
"image_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_... | code |
106192159/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() *... | code |
106192159/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train | code |
106192159/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_31 | [
"text_plain_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() ... | code |
106192159/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_... | code |
106192159/cell_5 | [
"image_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_train.isnull().sum() | code |
88102916/cell_21 | [
"text_plain_output_1.png"
] | import csv
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)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reade... | code |
88102916/cell_13 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_9 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter='... | code |
88102916/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Normalizer
import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encodin... | code |
88102916/cell_30 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encod... | code |
88102916/cell_20 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_40 | [
"text_plain_output_1.png"
] | import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter='... | code |
88102916/cell_29 | [
"image_output_1.png"
] | import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter='... | code |
88102916/cell_26 | [
"image_output_1.png"
] | import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter='... | code |
88102916/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, chi2
import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'r... | code |
88102916/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import csv
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88102916/cell_19 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_7 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
raw = open(path, 'rt', encoding='utf8')
hea... | code |
88102916/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', enc... | code |
88102916/cell_15 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_16 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_17 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_14 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter='... | code |
88102916/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Binarizer
import csv
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 seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding... | code |
88102916/cell_12 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.... | code |
88102916/cell_5 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
print(header)
data = list(reader)
data = np.array(data).astype('float')
print(data[0])
print(data.shap... | code |
121152041/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes | code |
121152041/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape | code |
121152041/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.head(10) | code |
121152041/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/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 |
121152041/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull() | code |
121152041/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
print(data_2022.shape)
data_2022.isnull().sum() | code |
121152041/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
121152041/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_202... | code |
1004254/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import ggplot
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
menu = pd.read_csv('../input/menu.csv')
menu.head(5)
df = menu | code |
1004254/cell_8 | [
"text_html_output_1.png"
] | df.sort_values(by='Protein', ascending=False).head(10) | code |
1004254/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(menu.describe()) | code |
1004254/cell_12 | [
"text_plain_output_1.png"
] | df.sort_values(by='Protein', ascending=False).head(10)
df.sort_values(by='Protein/Sugar', ascending=False).head(10) | code |
1004254/cell_5 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.pivot_table(df, index=['Category'], values=['Protein'], aggfunc=np.max).plot(kind='bar') | code |
106199411/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competiti... | code |
106199411/cell_9 | [
"image_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
... | code |
106199411/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv... | code |
106199411/cell_2 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', '... | code |
106199411/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
... | code |
106199411/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
... | code |
106199411/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'sho... | code |
88102789/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='embark_town', palette='deep') | code |
88102789/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='class', palette='deep') | code |
88102789/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.jointplot(data=df, x='age', y='fare', kind='scatter', color='c') | code |
88102789/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
df.info() | code |
88102789/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='class', hue='sex') | code |
88102789/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='embark_town', palette='Set2') | code |
88102789/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.kdeplot(df['age'], shade=True, color='m') | code |
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