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I have been trying to accomplish the following problem. There are dataframe 20x1 and another one 1x7. But after i tried to join both, i get a error returning NaN values.

  1. mean_control_chart it's the dataframe 1x7
  2. x_barra was a series, but a convert to a dict and after to a dataframe 20x1
  1. mean_control_chart dataframe:

enter image description here

  1. x_barra dataframe:

enter image description here

Code:

mean_control_chart = pd.DataFrame(df_mean_control_chart)
x_barra_dict = x_barra.to_dict()
x_barra_df = pd.DataFrame(x_barra_dict, index=['X-Barra']) 
x_barra_df = x_barra_df.transpose() 
mean_control_chart['X_Barra'] = x_barra_df

The error i get:

OBS: I want after that, the data at the 7 columns of mean_control_chart repet itself based of the x_barra len of rows. So if there are 20 or 100 lines in x_barra, the lines of mean_control_chart would repeat 20 or 100

Felipe Roque
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  • From your question it's not clear what was your expected outcome. How do you want to join the two DataFrames? Should the eight columns repeat the values for all the 20 rows of the other Dataframe? – pelelter Jan 29 '22 at 15:42
  • yes, that's exactly what i want – Felipe Roque Jan 29 '22 at 15:45

1 Answers1

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Please never post dataframes as pictures, always as text: How to make good reproducible pandas examples

As to your question, cross merge is probably the easiest. Demonstrating on a simple example (here dataframes provided by you would be useful):

df1 = pd.DataFrame(columns = list('ABCD'), data = [[1,2,3,4]])
df2 = pd.DataFrame(columns = ['E'], data= [[5],[6],[7],[8],[9]])
df1.merge(df2, how = 'cross')

output


    A   B   C   D   E
0   1   2   3   4   5
1   1   2   3   4   6
2   1   2   3   4   7
3   1   2   3   4   8
4   1   2   3   4   9
piterbarg
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