How to get the common index of two pandas dataframes?
up vote
4
down vote
favorite
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
values 1
0
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
01/12/2000 0.012749
04/12/2000 0.113892
df2
values 2
24/11/2000 -0.004808
27/11/2000 -0.001812
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
become
df1
value 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
df2
value 2
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
python pandas dataframe
asked Jan 9 at 14:54
astudentofmaths
370120
add a comment |
up vote
4
down vote
favorite
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
values 1
0
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
01/12/2000 0.012749
04/12/2000 0.113892
df2
values 2
24/11/2000 -0.004808
27/11/2000 -0.001812
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
become
df1
value 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
df2
value 2
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
python pandas dataframe
asked Jan 9 at 14:54
astudentofmaths
370120
add a comment |
up vote
4
down vote
favorite
up vote
4
down vote
favorite
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
values 1
0
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
01/12/2000 0.012749
04/12/2000 0.113892
df2
values 2
24/11/2000 -0.004808
27/11/2000 -0.001812
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
become
df1
value 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
df2
value 2
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
python pandas dataframe
asked Jan 9 at 14:54
astudentofmaths
370120
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
values 1
0
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
01/12/2000 0.012749
04/12/2000 0.113892
df2
values 2
24/11/2000 -0.004808
27/11/2000 -0.001812
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
become
df1
value 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
df2
value 2
28/11/2000 -0.026316
29/11/2000 0.015222
30/11/2000 -0.024480
python pandas dataframe
python pandas dataframe
asked Jan 9 at 14:54
astudentofmaths
370120
asked Jan 9 at 14:54
astudentofmaths
370120
asked Jan 9 at 14:54
astudentofmaths
370120
asked Jan 9 at 14:54
astudentofmaths
370120
asked Jan 9 at 14:54
astudentofmaths
370120
370120
add a comment |
add a comment |
7 Answers
7
active
oldest
votes
up vote
7
down vote
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 = df1.loc[idx]
print (df2)
values 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
answered Jan 9 at 14:57
jezrael
317k22257336
add a comment |
up vote
5
down vote
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
answered Jan 9 at 14:58
MaxU
119k11107165
add a comment |
up vote
4
down vote
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
answered Jan 9 at 15:07
Zero
38.2k86388
add a comment |
up vote
3
down vote
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
answered Jan 9 at 15:16
W-B
99k73162
add a comment |
up vote
1
down vote
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]]
answered Jan 9 at 14:57
LangeHaare
820720
add a comment |
up vote
0
down vote
You can pd.merge them with an intermediary DataFrame created with the indexes of the other DataFrame:
df2_indexes = pd.DataFrame(index=df2.index)
df1 = pd.merge(df1, df2_indexes, left_index=True, right_index=True)
df1_indexes = pd.DataFrame(index=df1.index)
df2 = pd.merge(df2, df1_indexes, left_index=True, right_index=True)
or you can use pd.eval:
df2_indexes = df2.index.values
df1 = df1[eval("df1.index in df2_indexes"]
df1_indexes = df1.index.values
df2 = df2[eval("df2.index in df1_indexes"]
answered Nov 22 at 16:24
starostise
11
add a comment |
up vote
0
down vote
The index object has some set-like properties so you simply can take the intersection as follows:
df1 = df1.reindex[ df1.index & df2.index ]
This retains the order of the first dataframe in the intersection, df.
answered Dec 8 at 18:51
iamjli
212
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7 Answers
7
active
oldest
votes
7 Answers
7
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
7
down vote
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 = df1.loc[idx]
print (df2)
values 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
answered Jan 9 at 14:57
jezrael
317k22257336
add a comment |
up vote
7
down vote
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 = df1.loc[idx]
print (df2)
values 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
answered Jan 9 at 14:57
jezrael
317k22257336
add a comment |
up vote
7
down vote
up vote
7
down vote
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 = df1.loc[idx]
print (df2)
values 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
answered Jan 9 at 14:57
jezrael
317k22257336
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 = df1.loc[idx]
print (df2)
values 1
28/11/2000 -0.055276
29/11/2000 0.027427
30/11/2000 0.066009
answered Jan 9 at 14:57
jezrael
317k22257336
answered Jan 9 at 14:57
jezrael
317k22257336
answered Jan 9 at 14:57
jezrael
317k22257336
answered Jan 9 at 14:57
jezrael
317k22257336
317k22257336
add a comment |
add a comment |
up vote
5
down vote
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
answered Jan 9 at 14:58
MaxU
119k11107165
add a comment |
up vote
5
down vote
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
answered Jan 9 at 14:58
MaxU
119k11107165
add a comment |
up vote
5
down vote
up vote
5
down vote
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
answered Jan 9 at 14:58
MaxU
119k11107165
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
answered Jan 9 at 14:58
MaxU
119k11107165
answered Jan 9 at 14:58
MaxU
119k11107165
answered Jan 9 at 14:58
MaxU
119k11107165
answered Jan 9 at 14:58
MaxU
119k11107165
119k11107165
add a comment |
add a comment |
up vote
4
down vote
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
answered Jan 9 at 15:07
Zero
38.2k86388
add a comment |
up vote
4
down vote
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
answered Jan 9 at 15:07
Zero
38.2k86388
add a comment |
up vote
4
down vote
up vote
4
down vote
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
answered Jan 9 at 15:07
Zero
38.2k86388
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
answered Jan 9 at 15:07
Zero
38.2k86388
answered Jan 9 at 15:07
Zero
38.2k86388
answered Jan 9 at 15:07
Zero
38.2k86388
answered Jan 9 at 15:07
Zero
38.2k86388
38.2k86388
add a comment |
add a comment |
up vote
3
down vote
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
answered Jan 9 at 15:16
W-B
99k73162
add a comment |
up vote
3
down vote
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
answered Jan 9 at 15:16
W-B
99k73162
add a comment |
up vote
3
down vote
up vote
3
down vote
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
answered Jan 9 at 15:16
W-B
99k73162
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
answered Jan 9 at 15:16
W-B
99k73162
answered Jan 9 at 15:16
W-B
99k73162
answered Jan 9 at 15:16
W-B
99k73162
answered Jan 9 at 15:16
W-B
99k73162
99k73162
add a comment |
add a comment |
up vote
1
down vote
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]]
answered Jan 9 at 14:57
LangeHaare
820720
add a comment |
up vote
1
down vote
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]]
answered Jan 9 at 14:57
LangeHaare
820720
add a comment |
up vote
1
down vote
up vote
1
down vote
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]]
answered Jan 9 at 14:57
LangeHaare
820720
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]]
answered Jan 9 at 14:57
LangeHaare
820720
answered Jan 9 at 14:57
LangeHaare
820720
answered Jan 9 at 14:57
LangeHaare
820720
answered Jan 9 at 14:57
LangeHaare
820720
820720
add a comment |
add a comment |
up vote
0
down vote
You can pd.merge them with an intermediary DataFrame created with the indexes of the other DataFrame:
df2_indexes = pd.DataFrame(index=df2.index)
df1 = pd.merge(df1, df2_indexes, left_index=True, right_index=True)
df1_indexes = pd.DataFrame(index=df1.index)
df2 = pd.merge(df2, df1_indexes, left_index=True, right_index=True)
or you can use pd.eval:
df2_indexes = df2.index.values
df1 = df1[eval("df1.index in df2_indexes"]
df1_indexes = df1.index.values
df2 = df2[eval("df2.index in df1_indexes"]
answered Nov 22 at 16:24
starostise
11
add a comment |
up vote
0
down vote
You can pd.merge them with an intermediary DataFrame created with the indexes of the other DataFrame:
df2_indexes = pd.DataFrame(index=df2.index)
df1 = pd.merge(df1, df2_indexes, left_index=True, right_index=True)
df1_indexes = pd.DataFrame(index=df1.index)
df2 = pd.merge(df2, df1_indexes, left_index=True, right_index=True)
or you can use pd.eval:
df2_indexes = df2.index.values
df1 = df1[eval("df1.index in df2_indexes"]
df1_indexes = df1.index.values
df2 = df2[eval("df2.index in df1_indexes"]
answered Nov 22 at 16:24
starostise
11
add a comment |
up vote
0
down vote
up vote
0
down vote
You can pd.merge them with an intermediary DataFrame created with the indexes of the other DataFrame:
df2_indexes = pd.DataFrame(index=df2.index)
df1 = pd.merge(df1, df2_indexes, left_index=True, right_index=True)
df1_indexes = pd.DataFrame(index=df1.index)
df2 = pd.merge(df2, df1_indexes, left_index=True, right_index=True)
or you can use pd.eval:
df2_indexes = df2.index.values
df1 = df1[eval("df1.index in df2_indexes"]
df1_indexes = df1.index.values
df2 = df2[eval("df2.index in df1_indexes"]
answered Nov 22 at 16:24
starostise
11
You can pd.merge them with an intermediary DataFrame created with the indexes of the other DataFrame:
df2_indexes = pd.DataFrame(index=df2.index)
df1 = pd.merge(df1, df2_indexes, left_index=True, right_index=True)
df1_indexes = pd.DataFrame(index=df1.index)
df2 = pd.merge(df2, df1_indexes, left_index=True, right_index=True)
or you can use pd.eval:
df2_indexes = df2.index.values
df1 = df1[eval("df1.index in df2_indexes"]
df1_indexes = df1.index.values
df2 = df2[eval("df2.index in df1_indexes"]
answered Nov 22 at 16:24
starostise
11
edited Nov 25 at 17:33
answered Nov 22 at 16:24
starostise
11
answered Nov 22 at 16:24
starostise
11
answered Nov 22 at 16:24
starostise
11
11
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The index object has some set-like properties so you simply can take the intersection as follows:
df1 = df1.reindex[ df1.index & df2.index ]
This retains the order of the first dataframe in the intersection, df.
answered Dec 8 at 18:51
iamjli
212
add a comment |
up vote
0
down vote
The index object has some set-like properties so you simply can take the intersection as follows:
df1 = df1.reindex[ df1.index & df2.index ]
This retains the order of the first dataframe in the intersection, df.
answered Dec 8 at 18:51
iamjli
212
add a comment |
up vote
0
down vote
up vote
0
down vote
The index object has some set-like properties so you simply can take the intersection as follows:
df1 = df1.reindex[ df1.index & df2.index ]
This retains the order of the first dataframe in the intersection, df.
answered Dec 8 at 18:51
iamjli
212
The index object has some set-like properties so you simply can take the intersection as follows:
df1 = df1.reindex[ df1.index & df2.index ]
This retains the order of the first dataframe in the intersection, df.
answered Dec 8 at 18:51
iamjli
212
answered Dec 8 at 18:51
iamjli
212
answered Dec 8 at 18:51
iamjli
212
answered Dec 8 at 18:51
iamjli
212
212
add a comment |
add a comment |
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