How to merge two dataframes side-by-side?

Question

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

Answer 1

• 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),
])

• Read more here - https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html

Answer 2

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

Answer 3

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

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)