Pandas: trouble understanding how merge works

Question

I'm doing something wrong with merge and I can't understand what it is. I've done the following to estimate a histogram of a series of integer values:

import pandas as pnd
import numpy  as np

series = pnd.Series(np.random.poisson(5, size = 100))
tmp  = {"series" : series, "count" : np.ones(len(series))}
hist = pnd.DataFrame(tmp).groupby("series").sum()
freq = (hist / hist.sum()).rename(columns = {"count" : "freq"})

If I print hist and freq this is what I get:

> print hist
        count
series       
0           2
1           4
2          13
3          15
4          12
5          16
6          18
7           7
8           8
9           3
10          1
11          1

> print freq 
        freq
series      
0       0.02
1       0.04
2       0.13
3       0.15
4       0.12
5       0.16
6       0.18
7       0.07
8       0.08
9       0.03
10      0.01
11      0.01

They're both indexed by "series" but if I try to merge:

> df   = pnd.merge(freq, hist, on = "series")

I get a KeyError: 'no item named series' exception. If I omit on = "series" I get a IndexError: list index out of range exception.

I don't get what I'm doing wrong. May be "series" is an index and not a column so I must do it differently?

Answer 1

From docs:

on: Columns (names) to join on. Must be found in both the left and right DataFrame objects. If not passed and left_index and right_index are False, the intersection of the columns in the DataFrames will be inferred to be the join keys

I don't know why this is not in the docstring, but it explains your problem.

You can either give left_index and right_index:

In : pnd.merge(freq, hist, right_index=True, left_index=True)
Out:
        freq  count
series
0       0.01      1
1       0.04      4
2       0.14     14
3       0.12     12
4       0.21     21
5       0.14     14
6       0.17     17
7       0.07      7
8       0.05      5
9       0.01      1
10      0.01      1
11      0.03      3

Or you can make your index a column and use on:

In : freq2 = freq.reset_index()

In : hist2 = hist.reset_index()

In : pnd.merge(freq2, hist2, on='series')
Out:
    series  freq  count
0        0  0.01      1
1        1  0.04      4
2        2  0.14     14
3        3  0.12     12
4        4  0.21     21
5        5  0.14     14
6        6  0.17     17
7        7  0.07      7
8        8  0.05      5
9        9  0.01      1
10      10  0.01      1
11      11  0.03      3

Alternatively and more simply, DataFrame has join method which does exactly what you want:

In : freq.join(hist)
Out:
        freq  count
series
0       0.01      1
1       0.04      4
2       0.14     14
3       0.12     12
4       0.21     21
5       0.14     14
6       0.17     17
7       0.07      7
8       0.05      5
9       0.01      1
10      0.01      1
11      0.03      3