path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89127402/cell_18 | [
"text_html_output_2.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_16 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from geopy.distance import distance
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Deter... | code |
89127402/cell_24 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_14 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import json
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import json
import plotly.graph_objs as go
import urllib.request
def read_geojson(url):
with urllib.request.urlopen(url... | code |
89127402/cell_22 | [
"text_html_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_10 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_27 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_12 | [
"text_html_output_1.png"
] | import json
import json
import plotly.graph_objs as go
import urllib.request
def read_geojson(url):
with urllib.request.urlopen(url) as url:
jdata = json.loads(url.read().decode())
return jdata
ireland_url = 'https://gist.githubusercontent.com/pnewall/9a122c05ba2865c3a58f15008548fbbd/raw/5bb4f84d918b8... | code |
49124403/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)
df_train = pd.read_csv('../input/jane-street-market-prediction/train.csv')
df_feature = pd.read_csv('../input/jane-street-market-prediction/features.csv')
df_test = pd.read_csv('../input/jane-street-market-prediction/example_te... | code |
49124403/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 |
49124403/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/jane-street-market-prediction/train.csv')
df_feature = pd.read_csv('../input/jane-street-market-prediction/features.csv')
df_test = pd.read_csv('../input/jane-street-market-prediction/example_te... | code |
49124403/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)
df_train = pd.read_csv('../input/jane-street-market-prediction/train.csv')
df_feature = pd.read_csv('../input/jane-street-market-prediction/features.csv')
df_test = pd.read_csv('../input/jane-street-market-prediction/example_te... | code |
49124403/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/jane-street-market-prediction/train.csv')
df_feature = pd.read_csv('../input/jane-street-market-prediction/features.csv')
df_test = pd.read_csv('../input/jane-street-market-prediction/example_te... | code |
330932/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_countries = pd.read... | code |
330932/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_duplicates('IndicatorCode') | code |
330932/cell_6 | [
"text_html_output_1.png"
] | from subprocess import check_output
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 |
330932/cell_2 | [
"text_html_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 |
330932/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indic... | code |
330932/cell_10 | [
"text_plain_output_1.png"
] | import sqlite3
import sqlite3
sqlite_file = '../input/database.sqlite'
conn = sqlite3.connect(sqlite_file)
c = conn.cursor()
c.execute("SELECT name FROM sqlite_master WHERE type='table'")
all_rows = c.fetchall()
print('1):', all_rows)
conn.close() | code |
330932/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_duplicates('IndicatorCode')
len(... | code |
18128922/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | w2v_model = gensim.models.word2vec.Word2Vec(size=W2V_SIZE, window=W2V_WINDOW, min_count=W2V_MIN_COUNT, workers=8) | code |
18128922/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V_SIZE = 300
W2V_WINDOW = 7
W2V_EPOCH = 32
W2V_MIN_COUN... | code |
18128922/cell_6 | [
"text_plain_output_1.png"
] | decode_map = {0: 'NEGATIVE', 2: 'NEUTRAL', 4: 'POSITIVE'}
def decode_sentiment(label):
return decode_map[int(label)]
df.target = df.target.apply(lambda x: decode_sentiment(x)) | code |
18128922/cell_2 | [
"text_plain_output_1.png"
] | import nltk
nltk.download('stopwords') | code |
18128922/cell_11 | [
"text_plain_output_1.png"
] | words = w2v_model.wv.vocab.keys()
vocab_size = len(words)
print('Vocab size', vocab_size) | code |
18128922/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.manifold import TSNE
from sklearn.feature_extraction... | code |
18128922/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V... | code |
18128922/cell_8 | [
"text_plain_output_1.png"
] | documents = [_text.split() for _text in df_train.text]
print('training tweets count', len(documents)) | code |
18128922/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | w2v_model.build_vocab(documents) | code |
18128922/cell_12 | [
"text_html_output_1.png"
] | w2v_model.train(documents, total_examples=len(documents), epochs=W2V_EPOCH) | code |
18128922/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V_SIZE = 300
W2V_WINDOW = 7
W2V_EPOCH = 32
W2V_MIN_COUN... | code |
33096179/cell_13 | [
"text_html_output_1.png"
] | from past.builtins import xrange
import pandas as pd
import seaborn as sns
webpth = 'http://www.files.benlaken.com/documents/'
monsoon = pd.read_csv('../input/Monsoon_data.csv', parse_dates=['Date'])
monsoon.index = monsoon.Date
monsoon = monsoon.drop('Date', 1)
olou = pd.read_csv('../input/Olou_counts.csv', parse_d... | code |
33096179/cell_6 | [
"image_output_1.png"
] | import pandas as pd
webpth = 'http://www.files.benlaken.com/documents/'
monsoon = pd.read_csv('../input/Monsoon_data.csv', parse_dates=['Date'])
monsoon.index = monsoon.Date
monsoon = monsoon.drop('Date', 1)
olou = pd.read_csv('../input/Olou_counts.csv', parse_dates=['Date'])
olou.index = olou.Date
olou = olou.drop('D... | code |
33096179/cell_2 | [
"image_output_1.png"
] | from __future__ import print_function, division, generators
import sys
print('Running Python {0}.{1}'.format(sys.version_info[:2][0], sys.version_info[:2][1]))
if sys.version_info[:2] > (3, 0):
print('Adding xrange for backwards compatibility'.format(sys.version_info[:2][0], sys.version_info[:2][1]))
from past.... | code |
33096179/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
webpth = 'http://www.files.benlaken.com/documents/'
monsoon = pd.read_csv('../input/Monsoon_data.csv', parse_dates=['Date'])
monsoon.index = monsoon.Date
monsoon = monsoon.drop('Date', 1)
olou = pd.read_csv('../input/Olou_counts.csv', parse_dates=['Date'])
olou.index = olou.Date
olou = olou.drop('D... | code |
33096179/cell_8 | [
"image_output_1.png"
] | import pandas as pd
webpth = 'http://www.files.benlaken.com/documents/'
monsoon = pd.read_csv('../input/Monsoon_data.csv', parse_dates=['Date'])
monsoon.index = monsoon.Date
monsoon = monsoon.drop('Date', 1)
olou = pd.read_csv('../input/Olou_counts.csv', parse_dates=['Date'])
olou.index = olou.Date
olou = olou.drop('D... | code |
33096179/cell_10 | [
"text_plain_output_1.png"
] | from past.builtins import xrange
import pandas as pd
webpth = 'http://www.files.benlaken.com/documents/'
monsoon = pd.read_csv('../input/Monsoon_data.csv', parse_dates=['Date'])
monsoon.index = monsoon.Date
monsoon = monsoon.drop('Date', 1)
olou = pd.read_csv('../input/Olou_counts.csv', parse_dates=['Date'])
olou.ind... | code |
73061721/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtrain['MSZoning'] | code |
73061721/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 |
73061721/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtrain['MSZoning']
dtrain = dtrain.drop(missing_data[missing_data['Total'] > 1].index, 1)
dtrain['Electrical'] = dtrain['Electrical'].fillna(dtrain['Electr... | code |
130001346/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
train.describe(include='all') | code |
130001346/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.Parch.astype(str) + ':' + train.SibSp.astype(str)
train.... | code |
130001346/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
def groupby_mean_sort(df, col):
return df[[col, 'Survived']].groupby([col], as_i... | code |
130001346/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
def groupby_mean_sort(df, col):
return df[[col, 'Survived']].groupby([col], as_i... | code |
130001346/cell_40 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.... | code |
130001346/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
def groupby_mean_sort(df, col):
return df[[col, 'Survived']].groupby([col], as_i... | code |
130001346/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
train.head(10) | code |
130001346/cell_45 | [
"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 numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.... | code |
130001346/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values... | code |
130001346/cell_47 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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 numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.... | code |
130001346/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.Parch.astype(str) + ':' + train.SibSp.astype(str)
train.... | code |
130001346/cell_43 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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 numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.... | code |
130001346/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.Parch.astype(str) + ':' + train.SibSp.astype(str)
train.... | code |
130001346/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
def groupby_mean_sort(df, col):
return df[[col, 'Survived']].groupby([col], as_i... | code |
130001346/cell_53 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.Parch.astype(str) + ':' + train.SibSp.astype(str)
train.... | code |
130001346/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
train.info()
test.info() | code |
130001346/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
Parch_and_SibSp_col = train.Parch.astype(str) + ':' + train.SibSp.astype(str)
train.... | code |
130001346/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
gender_submission_df = pd.read_csv('../input/titanic/gender_submission.csv')
def groupby_mean_sort(df, col):
return df[[col, 'Survived']].groupby([col], as_i... | code |
104127568/cell_21 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from datetime import timedelta
import pandas as pd
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
today_date = df['last_order_date'].max() + timedelta(days=2)
rfm_df = df.groupby('master_id... | code |
104127568/cell_9 | [
"text_html_output_1.png"
] | from datetime import timedelta
import pandas as pd
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
today_date = df['last_order_date'].max() + timedelta(days=2)
rfm_df = df.groupby('master_id... | code |
104127568/cell_11 | [
"text_html_output_1.png"
] | from datetime import timedelta
from plotly.offline import iplot
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
today_date = df['last_orde... | code |
104127568/cell_18 | [
"text_html_output_1.png"
] | from datetime import timedelta
from plotly.offline import iplot
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
today_date = df['last_orde... | code |
104127568/cell_15 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_3.png"
] | from datetime import timedelta
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from yellowbrick.cluster import KElbowVisualizer
import pandas as pd
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] ... | code |
104127568/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import timedelta
import pandas as pd
df = pd.read_csv('../input/flo-data2/flo_data_20k.csv')
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
today_date = df['last_order_date'].max() + timedelta(days=2)
rfm_df = df.groupby('master_id... | code |
106207199/cell_4 | [
"text_plain_output_1.png"
] | !pip install pandas | code |
128008954/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method | code |
128008954/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.info() | code |
128008954/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts() | code |
128008954/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df | code |
128008954/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/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 |
128008954/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue | code |
128008954/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape | code |
128008954/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum() | code |
128008954/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts()
df['WonBy'... | code |
128008954/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
128008954/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns
df.MatchNumber.value_counts()
df.Venue
df.shape
df.method
df.Margin.sum()
df['WonBy'][df.WonBy == 'Runs'].value_counts() | code |
128008954/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
df.columns | code |
128008954/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
df
matches = pd.read_csv('/kaggle/input/ipl-2008-to-2021-all-match-dataset/IPL_Matches_2008_2022.csv')
delivery = pd.read_csv('/kaggle/input/ipl-2008-to... | code |
33107759/cell_4 | [
"text_plain_output_1.png"
] | from cord import ResearchPapers
from cord import ResearchPapers
papers = ResearchPapers.load() | code |
33107759/cell_6 | [
"text_html_output_1.png"
] | from cord import ResearchPapers
from cord import ResearchPapers
papers = ResearchPapers.load()
covid_papers = papers.since_sarscov2()
covid_papers.searchbar('relationships between testing tracing efforts and public health outcomes') | code |
33107759/cell_2 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/dgunning/cord19.git | code |
128042012/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df_100 = df[df['Flight Distance'] > 100]
df_100.select... | code |
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