Pandas：根据其中一列的值将多个新列连接到现有数据帧

Question

Using Pandas, I have a data-frame in hand:

	A	B
0	a	9
1	b	9
2	c	9

Iterating through all rows (one by one), based on the A column, I load (from a ZipFile) an additional columns in the form of a dictionary (some of which are missing - need to eliminate the whole row). Result should look simillar to that:

	A	B	C	D
0	a	9	a-foo	a-bar
2	c	9	c-foo	c-bar

What is the best method to use for the iteration? Tried several options, among which are for-in, apply,map, but it usually fails on typing issues (I'm quite new to that).

Any help or directions will be truely appreciated.

Answer 1

Let's assume your data looks something like this:

import pandas as pd

data = {'A': {0: 'a', 1: 'b', 2: 'c'}, 
        'B': {0: 9, 1: 9, 2: 9}}

df = pd.DataFrame(data)

data2 = [{'A': 'a', 'C': 'a-foo', 'D': 'a-bar'},
         {'A': 'c', 'C': 'c-foo', 'D': 'c-bar'}]

df2 = pd.DataFrame(data2)

You can combine df.merge with df.dropna to merge the two dfs and drop the rows (here: only the row with index 1) that end up with NaN values in the process:

df.merge(df2, on='A', how='left').dropna(axis=0, how='any')

   A  B      C      D
0  a  9  a-foo  a-bar
2  c  9  c-foo  c-bar