Python[pandas]: Select certain rows by index of another dataframe

Question

I have a DataFrame and I would select only rows that contain index value into df1.index.

for Example:

In [96]: df Out[96]:    A  B  C  D 1  1  4  9  1 2  4  5  0  2 3  5  5  1  0 22 1  3  9  6 

and these indexes

In[96]:df1.index Out[96]: Int64Index([  1,   3,   4,   5,   6,   7,  22,  28,  29,  32,], dtype='int64', length=253) 

I would like this output:

In [96]: df Out[96]:    A  B  C  D 1  1  4  9  1 3  5  5  1  0 22 1  3  9  6 

Answer 1

Use isin:

df = df[df.index.isin(df1.index)] 

Or get all intersectioned indices and select by loc:

df = df.loc[df.index & df1.index] df = df.loc[np.intersect1d(df.index, df1.index)] df = df.loc[df.index.intersection(df1.index)] 

---

print (df)     A  B  C  D 1   1  4  9  1 3   5  5  1  0 22  1  3  9  6 

EDIT:

I tried solution: df = df.loc[df1.index]. Do you think that this solution is correct?

Solution is incorrect:

df = df.loc[df1.index] print (df)        A    B    C    D 1   1.0  4.0  9.0  1.0 3   5.0  5.0  1.0  0.0 4   NaN  NaN  NaN  NaN 5   NaN  NaN  NaN  NaN 6   NaN  NaN  NaN  NaN 7   NaN  NaN  NaN  NaN 22  1.0  3.0  9.0  6.0 28  NaN  NaN  NaN  NaN 29  NaN  NaN  NaN  NaN 32  NaN  NaN  NaN  NaN C:/Dropbox/work-joy/so/_t/t.py:23: FutureWarning:  Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative.  See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike   print (df)