How to get the common index of two pandas dataframes?

Question

I have two pandas DataFrames df1 and df2 and I want to transform them in order that they keep values only for the index that are common to the 2 dataframes.

df1

Answer 1

I found pd.Index and set combination much faster than numpy.intersect1d as well df1.index.intersection(df2.index). Here is what I used:

df2 = df2.loc[pd.Index(set(df1.index)&set(df2.index))]

Answer 2

Have you tried something like

df1 = df1.loc[[x for x in df1.index if x in df2.index]]
df2 = df2.loc[[x for x in df2.index if x in df1.index]]

Answer 3

In [352]: common = df1.index.intersection(df2.index)

In [353]: df1.loc[common]
Out[353]:
             values1
0
28/11/2000 -0.055276
29/11/2000  0.027427
30/11/2000  0.066009

In [354]: df2.loc[common]
Out[354]:
             values2
0
28/11/2000 -0.026316
29/11/2000  0.015222
30/11/2000 -0.024480

Answer 4

And, using isin. intersection might be faster though.

In [286]: df1.loc[df1.index.isin(df2.index)]
Out[286]:
             values1
0
28/11/2000 -0.055276
29/11/2000  0.027427
30/11/2000  0.066009

In [287]: df2.loc[df2.index.isin(df1.index)]
Out[287]:
             values2
0
28/11/2000 -0.026316
29/11/2000  0.015222
30/11/2000 -0.024480

Answer 5

You can use Index.intersection + DataFrame.loc:

idx = df1.index.intersection(df2.index)
print (idx)
Index(['28/11/2000', '29/11/2000', '30/11/2000'], dtype='object')

Alternative solution with numpy.intersect1d:

idx = np.intersect1d(df1.index, df2.index)
print (idx)
['28/11/2000' '29/11/2000' '30/11/2000']

---

df1 = df1.loc[idx]
print (df1)
            values 1
28/11/2000 -0.055276
29/11/2000  0.027427
30/11/2000  0.066009

df2 = df2.loc[idx]

Answer 6

reindex + dropna

df1.reindex(df2.index).dropna()
Out[21]: 
             values1
28/11/2000 -0.055276
29/11/2000  0.027427
30/11/2000  0.066009


df2.reindex(df1.index).dropna()
Out[22]: 
             values2
28/11/2000 -0.026316
29/11/2000  0.015222
30/11/2000 -0.024480