Pandas Concat increases number of rows

Question

I'm concatenating two dataframes, so I want to one dataframe is located to another. But first I did some transformation to initial dataframe:

scaler = MinMaxScaler() 
real_data = pd.DataFrame(scaler.fit_transform(df[real_columns]), columns = real_columns)

And then concatenate:

categorial_data  = pd.get_dummies(df[categor_columns], prefix_sep= '__')
train = pd.concat([real_data, categorial_data], axis=1, ignore_index=True)

I dont know why, but number of rows increased:

print(df.shape, real_data.shape, categorial_data.shape, train.shape)
(1700645, 23) (1700645, 16) (1700645, 130) (1703915, 146)

What happened and how fix the problem?

As you can see number of columns for train equals to sum of columns real_data and categorial_data

Answer 1

The problem is that sometimes when you perform several operations on a single dataframe object, the index persists in the memory. So using df.reset_index() will solve your problem.

Answer 2

I solved the problem by using hstack

train = pd.DataFrame(np.hstack([real_data,categorial_data]))