path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73074336/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df_Sort_by_adr = df.sort_values(by=['adr'], ascending=False)['name']
df_Sort_by_adr.dataFrame | code |
73074336/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.tail() | code |
73074336/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.isnull().sum() | code |
73074336/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.info() | code |
73074336/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df['country'].value_counts(sort=True)[:5] | code |
73074336/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns | code |
106195752/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
print(c.dty... | code |
106195752/cell_4 | [
"text_plain_output_1.png"
] | pip install numpy | code |
106195752/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print('rank: ', b.ndim) | code |
106195752/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(b.shape) | code |
106195752/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(b) | code |
106195752/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print('rank: ', a.ndim) | code |
106195752/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a.shape) | code |
106195752/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a.dtype) | code |
106195752/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a)
print(type(a)) | code |
106195752/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106195752/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
print('using built-in python function: ', time.time() - start)
start = time.time()
np.mean(x)
print('using NumPy: ', time.time() - start) | code |
2022470/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values | code |
2022470/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32)... | code |
2022470/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
sns.countplot('Sex', hue='Survived', data=df)
plt.show() | code |
2022470/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32)... | code |
2022470/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.head() | code |
2022470/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age_band'] = 2
df.loc[(df['Age'] ... | code |
2022470/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', encoding='ISO-8859-1', low_memory=False)
df.columns.values
df['Age_band'] = 0
df.loc[df['Age'] <= 16, 'Age_band'] = 0
df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age_band'] = 1
df.loc[(df['Age'] > 32)... | code |
16139957/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_4 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16139957/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16139957/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
rd = raw_data
rd = rd[rd['Latitude'] > rd['Latitude'].mean() - 3 * rd['Latitude'].std()][rd['Latitude'] < rd['Latitude'].mean() + 3 * rd['Latit... | code |
16139957/cell_12 | [
"text_plain_output_1.png"
] | GRID_X_DIM = 120
GRID_Y_DIM = 100
lng_step = (max_lng - min_lng) / (GRID_X_DIM - 1)
lat_step = (max_lat - min_lat) / (GRID_Y_DIM - 1)
(lng_step, lat_step) | code |
16139957/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/rows.csv', usecols=['CMPLNT_FR_DT', 'Latitude', 'Longitude']).dropna()
first_date = np.datetime64('2006-01-01')
days = (pd.to_datetime(raw_data['CMPLNT_FR_DT'], format='%m/%d/%Y... | code |
16123173/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import calendar
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df['Weekday'] = df.Date.dt.weekday
df['Weekday'] = df['Weekday'].apply(lambda x... | code |
16123173/cell_2 | [
"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)
df = pd.read_excel('../input/flight_schedule.xlsx')
df.head() | code |
16123173/cell_11 | [
"text_plain_output_1.png"
] | import calendar
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 = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df['Weekd... | code |
16123173/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
from datetime import datetime
import calendar
import matplotlib.pyplot as plt
import seaborn as sns
print(os.listdir('../input')) | code |
16123173/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import calendar
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df['Weekday'] = df.Date.dt.weekday
df['Weekday'] = df['Weekday'].apply(lambda x... | code |
16123173/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df.info() | code |
16123173/cell_10 | [
"text_html_output_1.png"
] | import calendar
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 = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df['Weekd... | code |
16123173/cell_5 | [
"image_output_1.png"
] | import calendar
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/flight_schedule.xlsx')
df.Date = pd.to_datetime(df.Date)
df.Time = df.Time.astype(str)
df.Time = pd.to_datetime(df.Time)
df['Weekday'] = df.Date.dt.weekday
df['Weekday'] = df['Weekday'].apply(lambda x... | code |
34126468/cell_13 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack | code |
34126468/cell_9 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits | code |
34126468/cell_25 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0] | code |
34126468/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple') | code |
34126468/cell_6 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana') | code |
34126468/cell_40 | [
"text_plain_output_1.png"
] | a = set('abracadabra')
b = set('alacazam')
a | code |
34126468/cell_29 | [
"text_plain_output_1.png"
] | v = ([1, 2, 3], [3, 2, 1])
v | code |
34126468/cell_39 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'crabgrass' in basket | code |
34126468/cell_26 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t | code |
34126468/cell_48 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
del tel['sape']
tel | code |
34126468/cell_11 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits
fruits.pop() | code |
34126468/cell_7 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4) | code |
34126468/cell_28 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t[0] = 88888 | code |
34126468/cell_8 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits | code |
34126468/cell_15 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack | code |
34126468/cell_16 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack.pop()
stack.pop()
stack | code |
34126468/cell_38 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'orange' in basket | code |
34126468/cell_47 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
tel['jack'] | code |
34126468/cell_35 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
x, y, z = t
print(x, y, z) | code |
34126468/cell_46 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel | code |
34126468/cell_14 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop() | code |
34126468/cell_10 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits | code |
34126468/cell_27 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
u = (t, (1, 2, 3, 4, 5))
u | code |
34126468/cell_37 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print(basket) | code |
34126468/cell_5 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine') | code |
106194155/cell_11 | [
"text_html_output_1.png"
] | data = port.copy()
data.head() | code |
106194155/cell_15 | [
"text_html_output_2.png"
] | import plotly.express as px
import plotly.express as px
data = port.copy()
data.Pstatus.value_counts()
import plotly.express as px
fig = px.funnel(data, x='sex', y='G3')
fig.show() | code |
106194155/cell_14 | [
"text_plain_output_1.png"
] | data = port.copy()
data.Pstatus.value_counts() | code |
106194155/cell_12 | [
"text_plain_output_1.png"
] | data = port.copy()
data.info() | code |
129007065/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.neighbors import NearestNeighbors
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)
from sklearn.neighbors import NearestNeighbors
from gensim.models import KeyedVect... | code |
129007065/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 |
129007065/cell_3 | [
"image_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.neighbors import NearestNeighbors
fr... | code |
72111100/cell_13 | [
"text_html_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.insert(0, 'brand', names)
train = train.drop(['name', 'seller_type', 'owner', 'torque', 'fuel'], axis=1)
train['engine'] = [int(x.split(' ')[0]) for x in list(train['engine'])]
train['mileage'] = [f... | code |
72111100/cell_25 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression as LR, Perceptron
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.... | code |
72111100/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression as LR, Perceptron
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.... | code |
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