How to merge two dataframes side-by-side?

is there a way to conveniently merge two data frames side by side?

both two data frames have 30 rows, they have different number of columns, say, df1 has 20 columns and df2 has 40 columns.

how can i easily get a new data frame of 30 rows and 60 columns?

df3 = pd.someSpecialMergeFunct(df1, df2)

or maybe there is some special parameter in append

df3 = pd.append(df1, df2, left_index=False, right_index=false, how='left')

ps: if possible, i hope the replicated column names could be resolved automatically.

thanks!

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You can use the concat function for this (axis=1 is to concatenate as columns):

pd.concat([df1, df2], axis=1)

See the pandas docs on merging/concatenating: http://pandas.pydata.org/pandas-docs/stable/merging.html

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I came across your question while I was trying to achieve something like the following:

Merge dataframe sideways

So once I sliced my dataframes, I first ensured that their index are the same. In your case both dataframes needs to be indexed from 0 to 29. Then merged both dataframes by the index.

df1.reset_index(drop=True).merge(df2.reset_index(drop=True), left_index=True, right_index=True)
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  • There is way, you can do it via a Pipeline.

** Use a pipeline to transform your numerical Data for ex-

Num_pipeline = Pipeline
([("select_numeric", DataFrameSelector([columns with numerical value])),
("imputer", SimpleImputer(strategy="median")),
])

**And for categorical data 

cat_pipeline = Pipeline([
("select_cat", DataFrameSelector([columns with categorical data])),
("cat_encoder", OneHotEncoder(sparse=False)),
])

** Then use a Feature union to add these transformations together

preprocess_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
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我发现其他的答案并不适合我，当我从谷歌进来的时候。

What I did instead was to set the new columns in place in the original df.

# list(df2.columns) gives you the column names of df2
# you then use these as the column names for df


df[ list(df2.columns) ] = df2
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If you want to combine 2 data frames with common column name, you can do the following:

df_concat = pd.merge(df1, df2, on='common_column_name', how='outer')
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This solution also works if df1 and df2 have different indices:

df1.loc[:, df2.columns] = df2.to_numpy()

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