Add multiple empty rows at beginning of a populated Dataframe with Python

I want to add multiple empty rows at start of my dataframe. I have tried using list but it dosen’t seem to return optimum result:

Example df:

Desired df:

Column names should also start from the nth row, I want to add n empty rows at the beginning of my Dataframe.

>Solution :

I’m not sure why you would want to do this but I did it by splitting up the original dataframe into a dataframe with a row of the column names and a separate dataframe of the data. I then created a dataframe of nans to be the blank rows and joined the 3 together. You will need to import numpy for this.

I created a variable no_cols to be the number of columns in the dataframe and no_empty_rows to be how many empty rows to simplify code:

no_cols = len(df.columns)
no_empty_rows = 6

Then I turned the columns into their own dataframe, with 1 row which is the column names, and headers as np.nan:

cols = pd.DataFrame([df.columns], columns = [np.nan]*no_cols)

    NaN   NaN   NaN   NaN
0  Col1  col2  col3  col4

Next I renamed the columns in the original dataframe to nan:

df.columns = [np.nan]*no_cols

   NaN  NaN    NaN   NaN
0  One  Two  Three  four
1    2    4      5     8

Then I created a new dataframe of nans, with 6 blank rows (this can be changed):

df_empty_rows = (pd.DataFrame(data=[[np.nan]*no_cols]*no_empty_rows,
                              columns=[np.nan]*no_cols,
                              index=[np.nan]*no_empty_rows))

     NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN
NaN  NaN  NaN  NaN  NaN

You can then append together all 3. First I put the columns and data of df back together and reset their index, then append that to df_empty_rows:

df_out = df_empty_rows.append(cols.append(df).reset_index(drop=True))

      NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
NaN   NaN   NaN    NaN   NaN
0.0  Col1  col2   col3  col4
1.0   One   Two  Three  four
2.0     2     4      5     8

Full code:

no_cols = len(df.columns)
no_empty_rows = 6
cols = pd.DataFrame([df.columns], columns=[np.nan]*no_cols)
df.columns = [np.nan]*no_cols
df_empty_rows = (pd.DataFrame(data=[[np.nan]*no_cols]*no_empty_rows,
                              columns=[np.nan]*no_cols,
                              index=[np.nan]*no_empty_rows))
df_out = df_empty_rows.append(cols.append(df).reset_index(drop=True))