Pandas: Creating DataFrame from Series

Question

My current code is shown below - I'm importing a MAT file and trying to create a DataFrame from variables within it:

mat = loadmat(file_path)  # load mat-file Variables = mat.keys()    # identify variable names  df = pd.DataFrame         # Initialise DataFrame  for name in Variables:      B = mat[name]     s = pd.Series (B[:,1]) 

So within the loop, I can create a series of each variable (they're arrays with two columns - so the values I need are in column 2)

My question is how do I append the series to the dataframe? I've looked through the documentation and none of the examples seem to fit what I'm trying to do.

Answer 1

Here is how to create a DataFrame where each series is a row.

For a single Series (resulting in a single-row DataFrame):

series = pd.Series([1,2], index=['a','b']) df = pd.DataFrame([series]) 

For multiple series with identical indices:

cols = ['a','b'] list_of_series = [pd.Series([1,2],index=cols), pd.Series([3,4],index=cols)] df = pd.DataFrame(list_of_series, columns=cols) 

For multiple series with possibly different indices:

list_of_series = [pd.Series([1,2],index=['a','b']), pd.Series([3,4],index=['a','c'])] df = pd.concat(list_of_series, axis=1).transpose() 

To create a DataFrame where each series is a column, see the answers by others. Alternatively, one can create a DataFrame where each series is a row, as above, and then use df.transpose(). However, the latter approach is inefficient if the columns have different data types.