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- import numpy as np
- import pandas as pd
- # простой пример
- df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B'])
- np.random.seed(123) # для генерации воспроизводимых значений
- # пример создания DataFrame со случайными данными различных типов
- df = pd.DataFrame({
- # some ways to create random data
- 'a':np.random.randn(6),
- 'b':np.random.choice( [5,7,np.nan], 6),
- 'c':np.random.choice( ['panda','python','shark'], 6),
- # some ways to create systematic groups for indexing or groupby
- # this is similar to r's expand.grid(), see note 2 below
- 'd':np.repeat( range(3), 2 ),
- 'e':np.tile( range(2), 3 ),
- # a date range and set of random dates
- 'f':pd.date_range('1/1/2011', periods=6, freq='D'),
- 'g':np.random.choice( pd.date_range('1/1/2011', periods=365,
- freq='D'), 6, replace=False)
- })
- a b c d e f g
- 0 -1.085631 NaN panda 0 0 2011-01-01 2011-08-12
- 1 0.997345 7 shark 0 1 2011-01-02 2011-11-10
- 2 0.282978 5 panda 1 0 2011-01-03 2011-10-30
- 3 -1.506295 7 python 1 1 2011-01-04 2011-09-07
- 4 -0.578600 NaN shark 2 0 2011-01-05 2011-02-27
- 5 1.651437 7 python 2 1 2011-01-06 2011-02-03
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