Question

我有两个看起来像这样的数据框：

2001-01-03 00:00:00      NaN      NaN      NaN      NaN  NaN
2001-01-03 00:01:00  0.95110  0.95110  0.95110  0.95110  4.0
2001-01-03 00:02:00  0.95100  0.95110  0.95100  0.95110  4.0
2001-01-03 00:03:00  0.95100  0.95100  0.95100  0.95100  4.0
2001-01-03 00:04:00  0.95090  0.95090  0.95090  0.95090  4.0
2001-01-03 00:05:00  0.95100  0.95100  0.95100  0.95100  4.0

我要做的是将一个df中的任何NaN行替换为另一个df中相同dateindex的行。

我试过这样的事情：

df = df.apply(lambda x: df2.ix[x['row']] if x.isnull().any() else x)

但它只是抛出了一堆错误，即使我可以让它工作也可能不是最优的方法。据我所知，有可能用.update（）来做，但是我无法理解它，所以如果有人能提供一些帮助我会非常感激。

Answer 1

您可以使用DataFrame.combine：

df = df1.combine_first(df2)

或DataFrame.fillna：

df = df1.fillna(df2)

或DataFrame.update：

df1.update(df2)
print (df1)

但DataFrames中需要相同的列名。

样品：

df1 = pd.DataFrame({1: {pd.Timestamp('2001-01-03 00:01:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:03:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:02:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): np.nan, pd.Timestamp('2001-01-03 00:05:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:04:00'): 0.95089999999999997}, 2: {pd.Timestamp('2001-01-03 00:01:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:03:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:02:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:00:00'): np.nan, pd.Timestamp('2001-01-03 00:05:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:04:00'): 0.95089999999999997}, 3: {pd.Timestamp('2001-01-03 00:01:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:03:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:02:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): np.nan, pd.Timestamp('2001-01-03 00:05:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:04:00'): 0.95089999999999997}, 4: {pd.Timestamp('2001-01-03 00:01:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:03:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:02:00'): 0.95109999999999995, pd.Timestamp('2001-01-03 00:00:00'): np.nan, pd.Timestamp('2001-01-03 00:05:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:04:00'): 0.95089999999999997}, 5: {pd.Timestamp('2001-01-03 00:01:00'): 4.0, pd.Timestamp('2001-01-03 00:03:00'): 4.0, pd.Timestamp('2001-01-03 00:02:00'): 4.0, pd.Timestamp('2001-01-03 00:00:00'): np.nan, pd.Timestamp('2001-01-03 00:05:00'): 4.0, pd.Timestamp('2001-01-03 00:04:00'): 4.0}})
df2 = pd.DataFrame({1: {pd.Timestamp('2001-01-03 00:01:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): 0.95089999999999997}, 2: {pd.Timestamp('2001-01-03 00:01:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): 0.95089999999999997}, 3: {pd.Timestamp('2001-01-03 00:01:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): 0.95089999999999997}, 4: {pd.Timestamp('2001-01-03 00:01:00'): 0.95099999999999996, pd.Timestamp('2001-01-03 00:00:00'): 0.95089999999999997}, 5: {pd.Timestamp('2001-01-03 00:01:00'): 4.0, pd.Timestamp('2001-01-03 00:00:00'): 4.0}})

print (df1)
                          1       2       3       4    5
2001-01-03 00:00:00     NaN     NaN     NaN     NaN  NaN
2001-01-03 00:01:00  0.9511  0.9511  0.9511  0.9511  4.0
2001-01-03 00:02:00  0.9510  0.9511  0.9510  0.9511  4.0
2001-01-03 00:03:00  0.9510  0.9510  0.9510  0.9510  4.0
2001-01-03 00:04:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:05:00  0.9510  0.9510  0.9510  0.9510  4.0

print (df2)
                          1       2       3       4    5
2001-01-03 00:00:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:01:00  0.9510  0.9510  0.9510  0.9510  4.0

df = df1.combine_first(df2)
print (df)
                          1       2       3       4    5
2001-01-03 00:00:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:01:00  0.9511  0.9511  0.9511  0.9511  4.0
2001-01-03 00:02:00  0.9510  0.9511  0.9510  0.9511  4.0
2001-01-03 00:03:00  0.9510  0.9510  0.9510  0.9510  4.0
2001-01-03 00:04:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:05:00  0.9510  0.9510  0.9510  0.9510  4.0

df = df1.fillna(df2)
print (df)
                          1       2       3       4    5
2001-01-03 00:00:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:01:00  0.9511  0.9511  0.9511  0.9511  4.0
2001-01-03 00:02:00  0.9510  0.9511  0.9510  0.9511  4.0
2001-01-03 00:03:00  0.9510  0.9510  0.9510  0.9510  4.0
2001-01-03 00:04:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:05:00  0.9510  0.9510  0.9510  0.9510  4.0

df1.update(df2)
print (df1)
                          1       2       3       4    5
2001-01-03 00:00:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:01:00  0.9510  0.9510  0.9510  0.9510  4.0
2001-01-03 00:02:00  0.9510  0.9511  0.9510  0.9511  4.0
2001-01-03 00:03:00  0.9510  0.9510  0.9510  0.9510  4.0
2001-01-03 00:04:00  0.9509  0.9509  0.9509  0.9509  4.0
2001-01-03 00:05:00  0.9510  0.9510  0.9510  0.9510  4.0

Python Pandas用另一个数据帧中具有相同dateindex的行替换NaN行

1 个答案: