if df['col']='a','b','c' and df2['col']='a123','b456','d789' how do I create df2['is_contained']='a','b','no_match' where if values from df['col'] are found within values from df2['col'] the df['col'] value is returned and if no match is found, 'no_match' is returned? Also I don't expect there to be multiple matches, but in the unlikely case there are, I'd want to return a string like 'Multiple Matches'.
3 Answers
With this toy data set, we want to add a new column to df2 which will contain no_match for the first three rows, and the last row will contain the value 'd' due to the fact that that row's col value (the letter 'a') appears in df1.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df1 = pd.DataFrame({'col': ['a', 'b', 'c', 'd']})
df2 = pd.DataFrame({'col': ['a123','b456','d789', 'a']})
In other words, values from df1 should be used to populate this new column in df2 only when a row's df2['col'] value appears somewhere in df1['col'].
In [2]: df1
Out[2]:
col
0 a
1 b
2 c
3 d
In [3]: df2
Out[3]:
col
0 a123
1 b456
2 d789
3 a
If this is the right way to understand your question, then you can do this with pandas isin:
In [4]: df2.col.isin(df1.col)
Out[4]:
0 False
1 False
2 False
3 True
Name: col, dtype: bool
This evaluates to True only when a value in df2.col is also in df1.col.
Then you can use np.where which is more or less the same as ifelse in R if you are familiar with R at all.
In [5]: np.where(df2.col.isin(df1.col), df1.col, 'NO_MATCH')
Out[5]:
0 NO_MATCH
1 NO_MATCH
2 NO_MATCH
3 d
Name: col, dtype: object
For rows where a df2.col value appears in df1.col, the value from df1.col will be returned for the given row index. In cases where the df2.col value is not a member of df1.col, the default 'NO_MATCH' value will be used.
2 Comments
ChrisArmstrong
I actually want it to match on a partial match. So in your example every value would have a match. I don't think isin handles partial matching.
gustavz
your output does not solve the question. he wanted to have a row wise comparison of two columns.
You must first guarantee that the indexes match. To simplify, I'll show as if the columns where in the same dataframe. The trick is to use the apply method in the columns axis:
df = pd.DataFrame({'col1': ['a', 'b', 'c', 'd'],
'col2': ['a123','b456','d789', 'a']})
df['contained'] = df.apply(lambda x: x.col1 in x.col2, axis=1)
df
col1 col2 contained
0 a a123 True
1 b b456 True
2 c d789 False
3 d a False
Comments
In 0.13, you can use str.extract:
In [11]: df1 = pd.DataFrame({'col': ['a', 'b', 'c']})
In [12]: df2 = pd.DataFrame({'col': ['d23','b456','a789']})
In [13]: df2.col.str.extract('(%s)' % '|'.join(df1.col))
Out[13]:
0 NaN
1 b
2 a
Name: col, dtype: object
'a's in'a123a', or do you mean in different rows of df2['col'], e.g.['a123','b456','a789']?