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7 lis 2016 · You can try these few ways to merge/join your dataframe. merge (inner join by default) df = pd.merge(df1, df2, left_index=True, right_index=True) join (left join by default) df = df1.join(df2) concat (outer join by default) df = pd.concat([df1, df2], axis=1)
Merge DataFrame or named Series objects with a database-style join. A named Series object is treated as a DataFrame with a single named column. The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored.
Inspired by the Fastest way to merge pandas dataframe on ranges, using pd.IntervalIndex you could create multiple intervals, (two in this case; one for ip_address and one for server_port):
You can add parameters left_index=True and right_index=True if you need merge by indexes in function merge: merge=pd.merge(df,d, how='inner', left_index=True, right_index=True) Sample (first value of index in d was changed for matching):
Merge DataFrame or named Series objects with a database-style join. A named Series object is treated as a DataFrame with a single named column. The join is done on columns or indexes.
pandas provides various methods for combining and comparing Series or DataFrame. concat (): Merge multiple Series or DataFrame objects along a shared index or column. DataFrame.join (): Merge multiple DataFrame objects along the columns. DataFrame.combine_first (): Update missing values with non-missing values in the same location.
5 sty 2022 · In this tutorial, you’ll learn how to combine data in Pandas by merging, joining, and concatenating DataFrames. You’ll learn how to perform database-style merging of DataFrames based on common columns or indices using the merge() function and the .join() method.
