Create pandas dataframe columnwise-CodePudding

I have a list of lists representing some data that I want to convert to a pandas dataframe.

Each of the list does not represent a row, but instead a column in the DataFrame.

ColumnNames = ['Col1', 'Col2']
MyData = [[1, 2, 3], ['Foo', 'Bar', 'Baz']]

Now if I just create a Dataframe from this data, it takes the values RowWise, which is not what I want. Because of that I also cannot give column names when creating the dataframe.

What I have seen is, create the DataFrame rowwise, transpose it, add column names.

MyDf = pd.DataFrame(MyData)    
MyDf = MyDf.transpose()
MyDf.columns = MyColums

This works, but looks a bit Hacky to me, requiring 3 instructions to create a Dataframe and also I'm not sure about the efficiency of first creating a Dataframe the wrong way and then having to transpose it, although I imagine that there's no actual data movement when transposing.

Question is: Is there a better way?

CodePudding user response：

Use zip:

MyDf = pd.DataFrame(zip(*MyData), columns=ColumnNames)

# OR

MyDf = pd.DataFrame({k: v for k, v in zip(ColumnNames, MyData)})

Output:

>>> MyDf
   Col1 Col2
0     1  Foo
1     2  Bar
2     3  Baz

Details about the transformation

>>> list(zip(*MyData))
[(1, 'Foo'), (2, 'Bar'), (3, 'Baz')]

CodePudding user response：

You can create the dataframe setting your columns as index, and transpose:

ColumnNames = ['Col1', 'Col2']
MyData = [[1, 2, 3], ['Foo', 'Bar', 'Baz']]

df = pd.DataFrame(MyData, index=ColumnNames).T

output:

  Col1 Col2
0    1  Foo
1    2  Bar
2    3  Baz