熊猫中两个数据框的简单连接

Question

I have two data frames in a Python program using Pandas. I am new to Pandas.

Each one has a number of columns and rows - the first is similar to:

   calc_1  calc_2 calc_3
0  34.3   43.1  42.0
2  3.0    4.0   5.0
3  6.1    6.1   6.2
4  4.2    4.3   4.5

The second is similar to:

   gender  age 
0  M      25
2  M      27
3  M      27
4  F      36

For every integer row index there is a corresponding entry in the second data frame. I would like to join them into a resulting data frame where the row indices are equal, such as an inner join in SQL.

I can't seem to get this correct. Append results in 2 x the number of rows I should have. The info is coming in from a CSV.

   calc_1  calc_2 calc_3  gender age
0  34.3   43.1  42.0      M      25
2  3.0    4.0   5.0       M      27
3  6.1    6.1   6.2       M      27
4  4.2    4.3   4.5       F      36

I would like to preserve the column order if possible when joining.

EDIT:

I can't seem to use merge because the integer index has no name

 pd.merge(df1, df2, on='?????', how='inner')

Answer 1

Use pd.concat and pass axis=1 to concatenate column-wise:

In [37]:

pd.concat([df,df1], axis=1)
Out[37]:
   calc_1  calc_2  calc_3 gender  age
0    34.3    43.1    42.0      M   25
2     3.0     4.0     5.0      M   27
3     6.1     6.1     6.2      M   27
4     4.2     4.3     4.5      F   36

or join :

In [38]:

df.join(df1)
Out[38]:
   calc_1  calc_2  calc_3 gender  age
0    34.3    43.1    42.0      M   25
2     3.0     4.0     5.0      M   27
3     6.1     6.1     6.2      M   27
4     4.2     4.3     4.5      F   36

Or merge and set left_index=True and right_index=True :

In [41]:

df.merge(df1, left_index=True, right_index=True)
Out[41]:
   calc_1  calc_2  calc_3 gender  age
0    34.3    43.1    42.0      M   25
2     3.0     4.0     5.0      M   27
3     6.1     6.1     6.2      M   27
4     4.2     4.3     4.5      F   36