In this dictionary, im getting error "ValueError: All arrays must be of the same length" can we repeat month name in month column

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This is a dictionary which i want to convert into a data frame but getting error "ValueError: All arrays must be of the same length" I want to try dataframe like in which year 2014 repeated in year column till the end and monthName column also repeated value in column like january,february,March,january,February so on, how can i do that ? is it possible to do that? Please help me

Dict_tab = {'Year': '2014',
           'monthName': ['January', 'February', 'March'],
           'Weekend': ['5','12','19','26','2','9','16','23','2','9','16','23','30'],
           'Avg': ['2.3','2','1','5','3','str','none','3','5','2','1','78','5']}
dataframe = pd.DataFrame(Dict_tab)
print(dataframe)
python
1 Answers

By creating a DataFrame using pandas, you are creating a table. For this reason, all columns must have the same number of values. The only exception to this is singular values, where it will be assumed that every value in that column will be the same. Because you provided 13 weekends and 13 averages but only 3 months, pandas doesn't know what the other 10 months are.

To solve this, you need to repeat the months so that all rows have a month provided.

For example, by assuming that the weekend increases for the month and decreases when going to the next month, you can assume that monthName should have the following assignment:

'monthName': ['January', 'January', 'January', 'January', 'February', 'February', 'February', 'February', 'March', 'March', 'March', 'March', 'March'],

This leads to the following output:

    Year monthName Weekend   Avg
0   2014   January       5   2.3
1   2014   January      12     2
2   2014   January      19     1
3   2014   January      26     5
4   2014  February       2     3
5   2014  February       9   str
6   2014  February      16  none
7   2014  February      23     3
8   2014     March       2     5
9   2014     March       9     2
10  2014     March      16     1
11  2014     March      23    78
12  2014     March      30     5
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