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First create a 6X4 matrix data.
dates = pd.date_range('20180830', periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D']) print(df)
Print:
A B C D 2018-08-30 0 1 2 3 2018-08-31 4 5 6 7 2018-09-01 8 9 10 11 2018-09-02 12 13 14 15 2018-09-03 16 17 18 19 2018-09-04 20 21 22 23
If we want to select the data in DataFrame
, two ways are described below, they can To achieve the same goal:
print(df['A']) print(df.A) """ 2018-08-30 0 2018-08-31 4 2018-09-01 8 2018-09-02 12 2018-09-03 16 2018-09-04 20 Freq: D, Name: A, dtype: int64 """
To make the selection span multiple rows or columns:
print(df[0:3]) """ A B C D 2018-08-30 0 1 2 3 2018-08-31 4 5 6 7 2018-09-01 8 9 10 11 """ print(df['20180830':'20180901']) """ A B C D 2018-08-30 0 1 2 3 2018-08-31 4 5 6 7 2018-09-01 8 9 10 11 """
If df[3:3]
will be an empty object. The latter selects data between the tags 20180830
to 20180901
, and includes these two tags .
You can also select via loc
, iloc
, ix
.
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A brief introduction to using the Pandas library to process big data in Python
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