path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2043738/cell_3 | [
"text_plain_output_1.png"
] | import calendar
import calendar
year = 2018
month = 1
for i in range(12):
print(calendar.month(year, month))
month += 1 | code |
32068018/cell_9 | [
"image_output_1.png"
] | import csv
import numpy as np
ROOT_PATH = '/kaggle/input/CORD-19-research-challenge/'
METADATA_PATH = f'{ROOT_PATH}/metadata.csv'
SAMPLE_SIZE = None
def create_embedding_dict(filepath, sample_size=None):
"""function to create embedding dictionary using given file"""
embedding_dict = {}
with open(filepath... | code |
32068018/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import csv
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import skfuzzy as fuzz
ROOT_PATH = '/kaggle/input/CORD-19-research-challenge/'
METADATA_PATH = f'{ROOT_PATH}/metadata.csv'
SAMPLE_SIZE = None
def create_embedding_dict(filepath, sample_size=N... | code |
32068018/cell_2 | [
"text_html_output_1.png"
] | # install required packages that are not available in the given environment
!pip install scikit-fuzzy | code |
32068018/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import csv
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import skfuzzy as fuzz
ROOT_PATH = '/kaggle/input/CORD-19-research-challenge/'
METADATA_PATH = f'{ROOT_PATH}/metadata.csv'
SAMPLE_SIZE = None
def create_embedding_dict(filepath, sample_size=N... | code |
32068018/cell_5 | [
"image_output_1.png"
] | import pandas as pd
ROOT_PATH = '/kaggle/input/CORD-19-research-challenge/'
METADATA_PATH = f'{ROOT_PATH}/metadata.csv'
SAMPLE_SIZE = None
meta_df = pd.read_csv(METADATA_PATH, dtype={'pubmed_id': str, 'Microsoft Academic Paper ID': str, 'doi': str})
meta_df.head() | code |
16123280/cell_21 | [
"text_plain_output_1.png"
] | size_list = [i for i in range(1, 10)]
size_list | code |
16123280/cell_13 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_25 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/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/argentina.csv')
data.info() | code |
16123280/cell_23 | [
"text_plain_output_1.png"
] | import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # seaborn to create grafic
data = pd.read_csv('../input/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_dro... | code |
16123280/cell_20 | [
"text_plain_output_1.png"
] | import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_dr... | code |
16123280/cell_26 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_2 | [
"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/argentina.csv')
data.head() | code |
16123280/cell_11 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_19 | [
"text_plain_output_1.png"
] | import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_dr... | code |
16123280/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
import os
print(os.listdir('../input')) | code |
16123280/cell_7 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
data.head() | code |
16123280/cell_18 | [
"text_plain_output_1.png"
] | import math
import math
array = [float(i) for i in range(1, 10)]
percent_bf = [math.log10(1 + 1 / d) for d in array]
percent_bf | code |
16123280/cell_8 | [
"text_plain_output_1.png"
] | [str(x) for x in range(10)] | code |
16123280/cell_15 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_16 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/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)
data = pd.read_csv('../input/argentina.csv')
data.STREET.value_counts().head() | code |
16123280/cell_17 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_24 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_14 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_22 | [
"text_plain_output_1.png"
] | import math
import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGI... | code |
16123280/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/cell_12 | [
"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/argentina.csv')
data.NUMBER = data.NUMBER.astype(str)
data.NUMBER = data.NUMBER.str.strip(' ')
data['DIGITO'] = data.NUMBER.map(lambda x: x[0])
index_drop = data[data.DIGITO == '0'].index.values
index_drop
data.drop(... | code |
16123280/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/argentina.csv')
data['NUMBER'].head() | code |
2034808/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2034808/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sampleSubmission = pd.read_csv('../input/samp... | code |
18146642/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels = data['target'].values
data = data.drop(['id', 'tar... | code |
18146642/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels = data['target'].values
data = data.drop(['id', 'tar... | code |
18146642/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18146642/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels = data['target'].values
data = data.drop(['id', 'tar... | code |
18146642/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels... | code |
18146642/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels = data['target'].values
data = data.drop(['id', 'target'], axis=1).values
from sklearn.model_selection i... | code |
18146642/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/train.csv')
labels... | code |
18131067/cell_13 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
print(df_ga... | code |
18131067/cell_6 | [
"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)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_g... | code |
18131067/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18131067/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_16 | [
"text_plain_output_1.png"
] | 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)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco'... | code |
18131067/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
print(df_games.columns) | code |
18131067/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
18131067/cell_12 | [
"text_plain_output_1.png"
] | 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)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco'... | code |
18131067/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_games = pd.read_csv('../input/games.csv')
keep = ['id', 'rated', 'turns', 'victory_status', 'winner', 'increment_code', 'white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'opening_ply']
df_games = df_games[keep]
df_minis =... | code |
2002649/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
for col in data... | code |
2002649/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
for col in data.columns:
data[col] =... | code |
2002649/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import *
from keras.layers import *
batch_size = 1
mlp_neurons = 5
neurons = 5
bi_neurons = 5
repeats = 5
nb_epochs = 5 | code |
2002649/cell_11 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
for col in data... | code |
2002649/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 |
2002649/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
for col in data.columns:
data[col] = labelencoder.fit_transform(data[col]... | code |
2002649/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
for col in data... | code |
16120909/cell_21 | [
"text_plain_output_1.png"
] | train_y.shape | code |
16120909/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'Notes'])
health_data.columns
health_data.isna().sum() | code |
16120909/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data.info() | code |
16120909/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
train_y.shape
model = LinearRegression()
model.fit(train_x, train_y)
model.coef_ | code |
16120909/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'Notes'])
health_data.columns | code |
16120909/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
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)
health_data = pd.read_csv('../input/Big_Ci... | code |
16120909/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16120909/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'Notes'])
health_data.columns
health_data.head() | code |
16120909/cell_17 | [
"text_html_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)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'No... | code |
16120909/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
train_y.shape
model = LinearRegression()
model.fit(train_x, train_y)
model.coef_
model.intercept_ | code |
16120909/cell_14 | [
"text_plain_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)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'No... | code |
16120909/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
train_y.shape
model = LinearRegression()
model.fit(train_x, train_y) | code |
16120909/cell_12 | [
"text_plain_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)
health_data = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_data = health_data.drop(columns=['BCHC Requested Methodology', 'Source', 'Methods', 'No... | code |
72076807/cell_4 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/30-days-of-ml/test.csv')
train = pd.read_csv('../input/30-days-of-ml/train.csv')
y = train.target
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_val... | code |
72076807/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 |
72076807/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/30-days-of-ml/test.... | code |
33118397/cell_9 | [
"text_plain_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-ch... | code |
33118397/cell_19 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
e... | code |
33118397/cell_7 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f... | code |
33118397/cell_18 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
e... | code |
33118397/cell_15 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-r... | code |
33118397/cell_16 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-r... | code |
33118397/cell_17 | [
"image_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-r... | code |
33118397/cell_10 | [
"text_plain_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-ch... | code |
33118397/cell_12 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-r... | code |
33118397/cell_5 | [
"image_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f... | code |
34127012/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')
data_df.isnull().sum()
data_df.dtypes | code |
34127012/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34127012/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum() | code |
34127012/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')
data_df.head(2) | code |
34127012/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df['Neighbourhood'].unique() | code |
34127012/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd2e9a_noshowappointments-kagglev2-may-2016/noshowappointments-kagglev2-may-2016.csv')
data_df.isnull().sum() | code |
105176386/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
partid... | code |
105176386/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
matche... | code |
105176386/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
players | code |
105176386/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
partid... | code |
105176386/cell_20 | [
"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)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
m... | code |
105176386/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
matche... | code |
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