How to keep index when using pandas merge
Question:
I would like to merge two DataFrames, and keep the index from the first frame as the index on the merged dataset. However, when I do the merge, the resulting DataFrame has integer index. How can I specify that I want to keep the index from the left data frame?
In [4]: a = pd.DataFrame({'col1': {'a': 1, 'b': 2, 'c': 3},
'to_merge_on': {'a': 1, 'b': 3, 'c': 4}})
In [5]: b = pd.DataFrame({'col2': {0: 1, 1: 2, 2: 3},
'to_merge_on': {0: 1, 1: 3, 2: 5}})
In [6]: a
Out[6]:
col1 to_merge_on
a 1 1
b 2 3
c 3 4
In [7]: b
Out[7]:
col2 to_merge_on
0 1 1
1 2 3
2 3 5
In [8]: a.merge(b, how='left')
Out[8]:
col1 to_merge_on col2
0 1 1 1.0
1 2 3 2.0
2 3 4 NaN
In [9]: _.index
Out[9]: Int64Index([0, 1, 2], dtype='int64')
EDIT: Switched to example code that can be easily reproduced
Answers:
In [5]: a.reset_index().merge(b, how="left").set_index('index')
Out[5]:
col1 to_merge_on col2
index
a 1 1 1
b 2 3 2
c 3 4 NaN
Note that for some left merge operations, you may end up with more rows than in a when there are multiple matches between a and b. In this case, you may need to drop duplicates.
There is a non-pd.merge solution using Series.map and DataFrame.set_index.
a['col2'] = a['to_merge_on'].map(b.set_index('to_merge_on')['col2']))
col1 to_merge_on col2
a 1 1 1.0
b 2 3 2.0
c 3 4 NaN
This doesn’t introduce a dummy index name for the index.
Note however that there is no DataFrame.map method, and so this approach is not for multiple columns.
df1 = df1.merge(df2, how="inner", left_index=True, right_index=True)
This allows to preserve the index of df1
You can make a copy of index on left dataframe and do merge.
a['copy_index'] = a.index
a.merge(b, how='left')
I found this simple method very useful while working with large dataframe and using pd.merge_asof() (or dd.merge_asof()).
This approach would be superior when resetting index is expensive (large dataframe).
Think I’ve come up with a different solution. I was joining the left table on index value and the right table on a column value based off index of left table. What I did was a normal merge:
First10ReviewsJoined = pd.merge(First10Reviews, df, left_index=True, right_on='Line Number')
Then I retrieved the new index numbers from the merged table and put them in a new column named Sentiment Line Number:
First10ReviewsJoined['Sentiment Line Number']= First10ReviewsJoined.index.tolist()
Then I manually set the index back to the original, left table index based off pre-existing column called Line Number (the column value I joined on from left table index):
First10ReviewsJoined.set_index('Line Number', inplace=True)
Then removed the index name of Line Number so that it remains blank:
First10ReviewsJoined.index.name = None
Maybe a bit of a hack but seems to work well and relatively simple. Also, guess it reduces risk of duplicates/messing up your data. Hopefully that all makes sense.
another simple option is to rename the index to what was before:
a.merge(b, how="left").set_axis(a.index)
merge preserves the order at dataframe ‘a’, but just resets the index so it’s safe to use set_axis
Assuming that the resulting df has the same number of rows and order as your first df, you can do this:
c = pd.merge(a, b, on='to_merge_on')
c.set_index(a.index,inplace=True)
For the people that wants to maintain the left index as it was before the left join:
def left_join(
a: pandas.DataFrame, b: pandas.DataFrame, on: list[str], b_columns: list[str] = None
) -> pandas.DataFrame:
if b_columns:
b_columns = set(on + b_columns)
b = b[b_columns]
df = (
a.reset_index()
.merge(
b,
how="left",
on=on,
)
.set_index(keys=[x or "index" for x in a.index.names])
)
df.index.names = a.index.names
return df
You can also use DataFrame.join() method to achieve the same thing. The join method will persist the original index. The column to join can be specified with on parameter.
In [17]: a.join(b.set_index("to_merge_on"), on="to_merge_on")
Out[17]:
col1 to_merge_on col2
a 1 1 1.0
b 2 3 2.0
c 3 4 NaN
I would like to merge two DataFrames, and keep the index from the first frame as the index on the merged dataset. However, when I do the merge, the resulting DataFrame has integer index. How can I specify that I want to keep the index from the left data frame?
In [4]: a = pd.DataFrame({'col1': {'a': 1, 'b': 2, 'c': 3},
'to_merge_on': {'a': 1, 'b': 3, 'c': 4}})
In [5]: b = pd.DataFrame({'col2': {0: 1, 1: 2, 2: 3},
'to_merge_on': {0: 1, 1: 3, 2: 5}})
In [6]: a
Out[6]:
col1 to_merge_on
a 1 1
b 2 3
c 3 4
In [7]: b
Out[7]:
col2 to_merge_on
0 1 1
1 2 3
2 3 5
In [8]: a.merge(b, how='left')
Out[8]:
col1 to_merge_on col2
0 1 1 1.0
1 2 3 2.0
2 3 4 NaN
In [9]: _.index
Out[9]: Int64Index([0, 1, 2], dtype='int64')
EDIT: Switched to example code that can be easily reproduced
In [5]: a.reset_index().merge(b, how="left").set_index('index')
Out[5]:
col1 to_merge_on col2
index
a 1 1 1
b 2 3 2
c 3 4 NaN
Note that for some left merge operations, you may end up with more rows than in a when there are multiple matches between a and b. In this case, you may need to drop duplicates.
There is a non-pd.merge solution using Series.map and DataFrame.set_index.
a['col2'] = a['to_merge_on'].map(b.set_index('to_merge_on')['col2']))
col1 to_merge_on col2
a 1 1 1.0
b 2 3 2.0
c 3 4 NaN
This doesn’t introduce a dummy index name for the index.
Note however that there is no DataFrame.map method, and so this approach is not for multiple columns.
df1 = df1.merge(df2, how="inner", left_index=True, right_index=True)
This allows to preserve the index of df1
You can make a copy of index on left dataframe and do merge.
a['copy_index'] = a.index
a.merge(b, how='left')
I found this simple method very useful while working with large dataframe and using pd.merge_asof() (or dd.merge_asof()).
This approach would be superior when resetting index is expensive (large dataframe).
Think I’ve come up with a different solution. I was joining the left table on index value and the right table on a column value based off index of left table. What I did was a normal merge:
First10ReviewsJoined = pd.merge(First10Reviews, df, left_index=True, right_on='Line Number')
Then I retrieved the new index numbers from the merged table and put them in a new column named Sentiment Line Number:
First10ReviewsJoined['Sentiment Line Number']= First10ReviewsJoined.index.tolist()
Then I manually set the index back to the original, left table index based off pre-existing column called Line Number (the column value I joined on from left table index):
First10ReviewsJoined.set_index('Line Number', inplace=True)
Then removed the index name of Line Number so that it remains blank:
First10ReviewsJoined.index.name = None
Maybe a bit of a hack but seems to work well and relatively simple. Also, guess it reduces risk of duplicates/messing up your data. Hopefully that all makes sense.
another simple option is to rename the index to what was before:
a.merge(b, how="left").set_axis(a.index)
merge preserves the order at dataframe ‘a’, but just resets the index so it’s safe to use set_axis
Assuming that the resulting df has the same number of rows and order as your first df, you can do this:
c = pd.merge(a, b, on='to_merge_on')
c.set_index(a.index,inplace=True)
For the people that wants to maintain the left index as it was before the left join:
def left_join(
a: pandas.DataFrame, b: pandas.DataFrame, on: list[str], b_columns: list[str] = None
) -> pandas.DataFrame:
if b_columns:
b_columns = set(on + b_columns)
b = b[b_columns]
df = (
a.reset_index()
.merge(
b,
how="left",
on=on,
)
.set_index(keys=[x or "index" for x in a.index.names])
)
df.index.names = a.index.names
return df
You can also use DataFrame.join() method to achieve the same thing. The join method will persist the original index. The column to join can be specified with on parameter.
In [17]: a.join(b.set_index("to_merge_on"), on="to_merge_on")
Out[17]:
col1 to_merge_on col2
a 1 1 1.0
b 2 3 2.0
c 3 4 NaN