Python将NaN行替换为另一个数据帧中具有相同日期索引的行

我有两个数据帧，看起来像这样：

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

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

我要做的是用另一个df中相同日期索引的行替换一个df中的任何NaN行

我试过这样的方法：

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

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，即使我能让它工作，也可能不是最理想的方法。 据我所知，使用.update（）可能是可行的，但我还没有弄清楚，因此如果有人能提供帮助，我将不胜感激。

您可以使用：

或：

或：

但在两个数据帧中都需要相同的列名

样本：

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.fillna（df2）

不起作用吗？先检查方法组合

df.combine_first（df2）

df1.update（df2，overwrite=False）-禁用覆盖，以便所有方法都给出相同的o/p？@NickilMaveli-谢谢。显然是的。

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