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I tried to find the answer in the official Pandas documentation, but found it more confusing than helpful. Basically I have two dataframes with overlapping, but not identical column lists:
df1:
A B
0 22 34
1 78 42
df2:
B C
0 76 29
1 11 67
I want to merge/concatenate/append them so that the result is
df3:
A B C
0 22 34 nan
1 78 42 nan
2 nan 76 29
3 nan 11 67
Should be fairly simple, but I've tried several intuitive approaches and always got errors. Can anybody help me?
856
Different column names are specified for merges in Pandas using the “left_on” and “right_on” parameters, instead of using only the “on” parameter. Merging dataframes with different names for the joining variable is achieved using the left_on and right_on arguments to the pandas merge function.
Use pandas. concat() to concatenate/merge two or multiple pandas DataFrames across rows or columns. When you concat() two pandas DataFrames on rows, it creates a new Dataframe containing all rows of two DataFrames basically it does append one DataFrame with another.
You can pass two DataFrame to be merged to the pandas. merge() method. This collects all common columns in both DataFrames and replaces each common column in both DataFrame with a single one.
It can be done using the merge() method. Below are some examples that depict how to merge data frames of different lengths using the above method: Example 1: Below is a program to merge two student data frames of different lengths.
If you just want to concatenate the dataframes you can use.
pd.concat([df1,df2])
output:
A B C
0 22.0 34 NaN
1 78.0 42 NaN
0 NaN 76 11.0
1 NaN 11 67.0
Then you can reset_index to recreate a simple incrementing index.
pd.concat([df,df2]).reset_index(drop = True)
Output:
A B C
0 22.0 34 NaN
1 78.0 42 NaN
2 NaN 76 11.0
3 NaN 11 67.0
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