例如,df1具有形状(533, 2176)
,索引如Elkford (5901003) DM 01010
,df2具有形状(743, 12)
,索引如5901003
;df1的索引括号中的数字将与df2的数字匹配。正如形状所示,有些指数根本不匹配。现在我需要一个具有shape(533, 2176+12)
的数据集,即在增加列的同时保留匹配的行。你知道吗
加载数据
import pandas as pd
from tabulate import tabulate
if __name__ == '__main__':
# Read data
census_subdivision_profile = pd.read_excel('../data/census_subdivision_profile.xlsx', sheetname='Data',
index_col='Geography', encoding='utf-8').T
print(tabulate(census_subdivision_profile.head(), headers="keys", index_col='CNSSSBDVSN', tablefmt='psql'))
print(census_subdivision_profile.shape)
census_subdivision_count = pd.read_csv('../data/augmented/census_subdivision.csv', encoding='utf-8')
print(tabulate(census_subdivision_count.head(), headers='keys', tablefmt='psql'))
print(census_subdivision_count.shape)
使用第一个答案,我得到了错误:
Traceback (most recent call last):
File "/Users/Chu/Documents/dssg/ongoing/economy_vs_tourism.py", line 26, in <module>
census_subdivision_profile.index = census_subdivision_profile.index.map(extract_id)
File "/anaconda/lib/python2.7/site-packages/pandas/core/indexes/base.py", line 2727, in map
mapped_values = self._arrmap(self.values, mapper)
File "pandas/_libs/algos_common_helper.pxi", line 1212, in pandas._libs.algos.arrmap_object (pandas/_libs/algos.c:31954)
File "/Users/Chu/Documents/dssg/ongoing/economy_vs_tourism.py", line 10, in extract_id
return int(m.group(0)[1:-1])
ValueError: invalid literal for int() with base 10: 'Part 1) (5917054'
只是因为
Index([u'Canada (01) 20000',
u'British Columbia / Colombie-Britannique (59) 21010',
u'East Kootenay (5901) 01010', u'Elkford (5901003) DM 01010',
u'Sparwood (5901006) DM 01010', u'Fernie (5901012) CY 01010',
u'East Kootenay A (5901017) RDA 02020',
u'East Kootenay B (5901019) RDA 01020', u'Cranbrook (5901022) CY 01011',
u'Kimberley (5901028) CY 01010',
另一个是
Int64Index([5931813, 5941833, 5949832, 5919012, 5923033, 5924836, 5941016,
5955040, 5923809, 5941801,
数据框太大了对不起,我不能放在这里
文件1.csv:
这里
df1.shape = (10, 2)
。你知道吗文件2.csv:
这里
df2.shape = (3, 1)
。你知道吗运行此脚本:
输出:
这里
df.shape = (10, 2 + 1)
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