数据格式转换与迭代

1、 List转Numpy

aList = [1,2,3,4,5]
bList = [1,3,5,7,9]

aList+bList

[1, 2, 3, 4, 5, 1, 3, 5, 7, 9]

import numpy as np

aArray = np.array(aList)
bArray = np.array(bList)

aArray+bArray

array([ 2,  5,  8, 11, 14])

2、 Numpy转Series

import pandas as pd

aSeries = pd.Series(aArray, index=pd.date_range("20180101",periods=len(aArray), freq="D"))
bSeries = pd.Series(bArray, index=pd.date_range("20180101",periods=len(bArray), freq="D"))

cSeries = aSeries+bSeries
dSeries = aSeries*bSeries

3、 Series转DataFrame

abDF = pd.DataFrame({'a': aSeries, 'b':bSeries})

abDF

	a	b
2018-01-01	1	1
2018-01-02	2	3
2018-01-03	3	5
2018-01-04	4	7
2018-01-05	5	9

pd.concat([aSeries, bSeries], axis=1, keys=['a','b'])

	a	b
2018-01-01	1	1
2018-01-02	2	3
2018-01-03	3	5
2018-01-04	4	7
2018-01-05	5	9

cdDF = pd.DataFrame({'a': cSeries, 'b':dSeries})

4、 DataFrame转Panel

abcdPN = pd.Panel({'abDF': abDF, 'cdDF': cdDF})

abcdPN.transpose(2,1,0).to_frame(False)

		a	b
major	minor
2018-01-01	abDF	1	1
	cdDF	2	1
2018-01-02	abDF	2	3
	cdDF	5	6
2018-01-03	abDF	3	5
	cdDF	8	15
2018-01-04	abDF	4	7
	cdDF	11	28
2018-01-05	abDF	5	9
	cdDF	14	45

5、 iteritem()

计算每一列的加总

{item: value.sum() for item, value in abDF.iteritems()}

{'a': 15, 'b': 25}

6、 iterrow()

计算每一行的加总

{index: value.sum() for index, value in abDF.iterrows()}

{Timestamp('2018-01-01 00:00:00', freq='D'): 2,
 Timestamp('2018-01-02 00:00:00', freq='D'): 5,
 Timestamp('2018-01-03 00:00:00', freq='D'): 8,
 Timestamp('2018-01-04 00:00:00', freq='D'): 11,
 Timestamp('2018-01-05 00:00:00', freq='D'): 14}