python2 pandas：如何将另一个数据框的一部分合并到一个数据框 [英] python2 pandas: how to merge a part of another dataframe to a dataframe

问题描述

我有一个dataframe（df1）如下：

I have a dataframe(df1) as following:

       datetime     m  d    1d    2d   3d
       2014-01-01   1  1     2     2   3
       2014-01-02   1  2     3     4   3
       2014-01-03   1  3     1     2   3
       ...........
       2014-12-01  12  1      2     2   3
       2014-12-31  12  31     2     2   3

我还有另一个数据框（df2），如下所示：

Also I have another dataframe(df2) as following:

       datetime     m  d       
       2015-01-02   1  2     
       2015-01-03   1  3     
       ...........
       2015-12-01  12  1      
       2015-12-31  12  31     

我想将df1的1d 2d 3d列值合并到df2。
有两个条件：
（1）在df1和df2中，只有m和d相同，才能合并。
（2）如果df2索引％30 == 0的索引不合并，则这些索引的1d 2d 3d值可以为Nan。

I want to merge the 1d 2d 3d columns value of df1 to df2. There are two conditions: (1) only m and d are the same in both df1 and df2 can merge. (2) if the index of df2 index % 30 ==0 don't merge, the value of 1d 2d 3d of these index can be Nan.

I mean I want the new dataframe of df2 like as following:

       datetime     m  d    1d    2d   3d
       2015-01-02   1  2   Nan     Nan   Nan
       2015-01-03   1  3     1     2   3
       ...........
       2015-12-01  12  1      2     2   3
       2015-12-31  12  31     2     2   3

谢谢！

推荐答案

我认为您需要通过NaN s /pandas-docs/stable/generation/pandas.DataFrame.loc.html rel = nofollow noreferrer> loc ，然后 合并 加入：

I think you need add NaNs by loc and then merge with left join:

np.random.seed(10)
N = 365
rng = pd.date_range('2015-01-01', periods=N)
df_tr_2014 = pd.DataFrame(np.random.randint(10, size=(N, 3)), index=rng).reset_index()
df_tr_2014.columns = ['datetime','7d','15d','20d']
df_tr_2014.insert(1,'month', df_tr_2014['datetime'].dt.month)
df_tr_2014.insert(2,'day_m', df_tr_2014['datetime'].dt.day)
#print (df_tr_2014.head())

N = 366
rng = pd.date_range('2016-01-01', periods=N)
df_te = pd.DataFrame(index=rng)
df_te['month'] = df_te.index.month
df_te['day_m'] = df_te.index.day
df_te = df_te.reset_index()
#print (df_te.tail())
df2 = df_te.copy()
df1 = df_tr_2014.copy()

---

---

df1 = df1.set_index('datetime')
df1.index += pd.offsets.DateOffset(years=1)

#correct 29 February
y = df1.index[0].year
df1 = df1.reindex(pd.date_range(pd.datetime(y,1,1), pd.datetime(y,12,31)))
idx = df1.index[(df1.index.month == 2) & (df1.index.day == 29)]

df1.loc[idx, :] = df1.loc[idx - pd.Timedelta(1, unit='d'), :].values
df1.loc[idx, 'day_m'] = idx.day
df1[['month','day_m']] = df1[['month','day_m']].astype(int)

df1[['7d','15d', '20d']] = df1[['7d','15d', '20d']].astype(float)

df1.loc[np.arange(len(df1.index))  % 30 == 0, ['7d','15d','20d']] = 0
df1 = df1.reset_index()
print (df1.iloc[57:62])
        index  month  day_m   7d  15d  20d
57 2016-02-27      2     27  2.0  0.0  1.0
58 2016-02-28      2     28  2.0  3.0  5.0
59 2016-02-29      2     29  2.0  3.0  5.0
60 2016-03-01      3      1  0.0  0.0  0.0
61 2016-03-02      3      2  7.0  6.0  9.0

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