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As the title, I have one column (series) in pandas, and each row of it is a list like [0,1,2,3,4,5]. Each list has 6 numbers. I want to change this column into 6 columns, for example, the [0,1,2,3,4,5] will become 6 columns, with 0 is the first column, 1 is the second, 2 is the third and so on. How can I make it?
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split() function is used to break up single column values into multiple columns based on a specified separator or delimiter. The Series. str. split() function is similar to the Python string split() method, but split() method works on the all Dataframe columns, whereas the Series.
Pandas DataFrame - expanding() functionThe expanding() function is used to provide expanding transformations. Minimum number of observations in window required to have a value (otherwise result is NA). Set the labels at the center of the window.
Select the cell or column that contains the text you want to split. Select Data > Text to Columns. In the Convert Text to Columns Wizard, select Delimited > Next. Select the Delimiters for your data.
Add multiple columns to a data frame using Dataframe. assign() method. Using DataFrame. assign() method, we can set column names as parameters and pass values as list to replace/create the columns.
Not as fast as @jezrael's solution. But elegant :-)
apply with pd.Series
df.a.apply(pd.Series)
0 1 2 3 4 5
0 0 1 2 3 4 5
1 0 1 2 3 4 5
or
df.a.apply(pd.Series, index=list('abcdef'))
a b c d e f
0 0 1 2 3 4 5
1 0 1 2 3 4 5
You can convert lists to numpy array by values and then use DataFrame constructor:
df = pd.DataFrame({'a':[[0,1,2,3,4,5],[0,1,2,3,4,5]]})
print (df)
a
0 [0, 1, 2, 3, 4, 5]
1 [0, 1, 2, 3, 4, 5]
df1 = pd.DataFrame(df['a'].values.tolist())
print (df1)
0 1 2 3 4 5
0 0 1 2 3 4 5
1 0 1 2 3 4 5
cols = list('abcdef')
df1 = pd.DataFrame(df['a'].values.tolist(), columns=cols)
print (df1)
a b c d e f
0 0 1 2 3 4 5
1 0 1 2 3 4 5
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