Use np.repeat:

Version 1:

Try using np.repeat:

newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0))newdf.columns = df.columnsprint(newdf)

The above code will output:

  Person   ID ZipCode  Gender0  12345  882   38182  Female1  12345  882   38182  Female2  12345  882   38182  Female3  32917  271   88172    Male4  32917  271   88172    Male5  32917  271   88172    Male6  18273  552   90291  Female7  18273  552   90291  Female8  18273  552   90291  Female

np.repeat repeats the values of df, 3 times.

Then we add the columns with assigning new_df.columns = df.columns.

Version 2:

You could also assign the column names in the first line, like below:

newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0), columns=df.columns)print(newdf)

The above code will also output:

  Person   ID ZipCode  Gender0  12345  882   38182  Female1  12345  882   38182  Female2  12345  882   38182  Female3  32917  271   88172    Male4  32917  271   88172    Male5  32917  271   88172    Male6  18273  552   90291  Female7  18273  552   90291  Female8  18273  552   90291  Female

These will repeat the indices and preserve the columns as op demonstrated

iloc version 1

df.iloc[np.arange(len(df)).repeat(3)]

iloc version 2

df.iloc[np.arange(len(df) * 3) // 3]

Using concat:

pd.concat([df]*3).sort_index()Out[129]:    Person   ID  ZipCode  Gender0   12345  882    38182  Female0   12345  882    38182  Female0   12345  882    38182  Female1   32917  271    88172    Male1   32917  271    88172    Male1   32917  271    88172    Male2   18273  552    90291  Female2   18273  552    90291  Female2   18273  552    90291  Female