I need to generate a data frame with as many columns as the row indicates-CodePudding

I tought using Transpose but it doesn't work, Please help, I have this input dataframe

And i need this Output

 number  C1   C2   C3   C4   C5   C6
   1      1   nan  nan  nan  nan  nan
   2      1    2   nan  nan  nan  nan
   3      1    2    3   nan  nan  nan
   4      1    2    3    4   nan  nan
   6      1    2    3    4    5    6

CodePudding user response：

I solved my own question, i post my solution here it will surely help someone.

max=df1["number"].max()
for i in range(1,max 1):
    df1["column"   str(i)]=np.where(df1["number"] >=(i) , i, np.nan)

CodePudding user response：

Try this:

(df.join(pd.DataFrame(
    df['number']
    .map(lambda x: range(1,x 1)).tolist())
    .rename(lambda x: 'C{}'.format(x 1),axis=1)))

Output:

   number  C1   C2   C3   C4   C5   C6
0       1   1  NaN  NaN  NaN  NaN  NaN
1       2   1  2.0  NaN  NaN  NaN  NaN
2       3   1  2.0  3.0  NaN  NaN  NaN
3       4   1  2.0  3.0  4.0  NaN  NaN
4       6   1  2.0  3.0  4.0  5.0  6.0

CodePudding user response：

Welcome to StackOverflow!

import pandas as pd

# Creates the DF
input_df = pd.DataFrame([1, 2, 3, 4, 5, 6], columns=['number'])

# Creates a list from the values in the 'number' column
values_in_column = input_df.number.tolist()

# For each values in our list, create a column in the DF
for val in values_in_column:
    input_df[val] = None

# For each row in the input DF: 
for index, row in input_df.iterrows():

    # Iterate over each column (except the first one)
    for col_val in input_df.columns[1:]:

        # If the value in 'number' at this row is greater of equal to the value of the column header
        if row['number'] >= col_val:

            # Enter the value of the column into the DF
            input_df.at[index, col_val] = col_val

This should do what you're looking for. Note, when I make the dataframe, the column headers are integers and not strings like in your question ('C1', 'C2', etc.).