我有一个熊猫数据框，其中包含年月数据列（yyyymm）。我正计划将数据插值为每日和每周值。这是我的df。
df:
201301 201302 201303 ... 201709 201710
a 0.747711 0.793101 0.771819 ... 0.818161 0.812522
b 0.776537 0.759745 0.733673 ... 0.757496 0.765181
c 0.801699 0.847655 0.796586 ... 0.784537 0.763551
d 0.797942 0.687899 0.729911 ... 0.819887 0.772395
e 0.777472 0.799676 0.782947 ... 0.804533 0.791759
f 0.780933 0.750774 0.781056 ... 0.790846 0.773705
g 2.071699 2.261739 2.126915 ... 1.891780 2.098914
如您所见，我的df位于蒙特列数据中，我希望将其更改为每日值。我打算使用线性函数。这是例子。
# (201302 - 201301)/31 (since January 2013 has 31 days)
a = (0.793101-0.747711)/31
# now a is the daily increasing (or decresing depends on values) value for a day.
# 2013-01-01 value woud be
0.747711
# 2013-01-02 value woud be
0.747711 + a
# 2013-01-03 value woud be
0.747711 + (a*2)
# last day of January would be
0.747711 + (a*30)
# first day of Feb would be
0.747711 + (a*31) which is 0.793101 (201302 value)
所以我的df_daily从2013年到2017年10月（第一天）每天都有，其值与上面一样。我非常忙于处理时间戳，因此，如果有任何方法可以将我的值从月值转换为日值，那将是很棒的。谢谢！
哦，请让我知道我的问题是否令人困惑...
最佳答案
首先将datetimes的列转换为to_datetime，然后将reindex的NaN转换为缺少的日期，最后interpolate：
df.columns = pd.to_datetime(df.columns, format='%Y%m')
#by first and last values of columns
rng = pd.date_range(df.columns[0], df.columns[-1])
#alternatively min and max of columns
#rng = pd.date_range(df.columns.min(), df.columns.max())
df = df.reindex(rng, axis=1).interpolate(axis=1)
验证解决方案：
a = (0.793101-0.747711)/31
print (0.747711 + a)
print (0.747711 + a*2)
print (0.747711 + a*3)
0.7491751935483871
0.7506393870967742
0.7521035806451613
print (df)
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 \
a 0.747711 0.749175 0.750639 0.752104 0.753568 0.755032
b 0.776537 0.775995 0.775454 0.774912 0.774370 0.773829
c 0.801699 0.803181 0.804664 0.806146 0.807629 0.809111
d 0.797942 0.794392 0.790842 0.787293 0.783743 0.780193
e 0.777472 0.778188 0.778905 0.779621 0.780337 0.781053
f 0.780933 0.779960 0.778987 0.778014 0.777042 0.776069
g 2.071699 2.077829 2.083960 2.090090 2.096220 2.102351
2013-01-07 2013-01-08 2013-01-09 2013-01-10 ... 2017-09-22 \
a 0.756496 0.757960 0.759425 0.760889 ... 0.814214
b 0.773287 0.772745 0.772204 0.771662 ... 0.762876
c 0.810594 0.812076 0.813559 0.815041 ... 0.769847
d 0.776643 0.773094 0.769544 0.765994 ... 0.786643
e 0.781770 0.782486 0.783202 0.783918 ... 0.795591
f 0.775096 0.774123 0.773150 0.772177 ... 0.778847
g 2.108481 2.114611 2.120742 2.126872 ... 2.036774
2017-09-23 2017-09-24 2017-09-25 2017-09-26 2017-09-27 2017-09-28 \
a 0.814026 0.813838 0.813650 0.813462 0.813274 0.813086
b 0.763132 0.763388 0.763644 0.763900 0.764156 0.764413
c 0.769147 0.768448 0.767748 0.767049 0.766349 0.765650
d 0.785060 0.783476 0.781893 0.780310 0.778727 0.777144
e 0.795165 0.794740 0.794314 0.793888 0.793462 0.793036
f 0.778276 0.777705 0.777133 0.776562 0.775990 0.775419
g 2.043678 2.050583 2.057487 2.064392 2.071296 2.078201
2017-09-29 2017-09-30 2017-10-01
a 0.812898 0.812710 0.812522
b 0.764669 0.764925 0.765181
c 0.764950 0.764251 0.763551
d 0.775561 0.773978 0.772395
e 0.792611 0.792185 0.791759
f 0.774848 0.774276 0.773705
g 2.085105 2.092010 2.098914
[7 rows x 1735 columns]