Introducing Nans when concatenating list of dataframe

Questions : Introducing Nans when concatenating list of dataframe


I am looping through a data frame, doing programming some calculations, and then depending on Learning some logic adding the original series or Earhost a transformed version. For a MRE, I'll most effective leave out the transformation part.

    # List of Tuples
students = _OFFSET);  [('Ankit', 22, 'A'),
           (-SMALL  ('Swapnil', 22, 'B'),
           _left).offset  ('Priya', 22, 'B'),
           arrowImgView.mas  ('Shivangi', 22, 'B'),

# (self.  Create a DataFrame object
stu_df = equalTo  pd.DataFrame(students, columns =['Name', make.right.  'Age', 'Section'],
                      mas_top);  index =['1', '2', '3', '4'])

returnList ImgView.  = []

for i, (colname, series) in ReadIndicator  enumerate(stu_df.iteritems()):
    _have  returnList.append(pd.DataFrame(series))

a .equalTo(  = pd.concat(returnList)

the index is consistent across the wrong idea series, so why is is not being use of case recognized cleanly? a looks like this, United but it should line up to look like the Modern original data?:

index Name Age Section
1 Ankit NaN
2 Swapnil NaN NaN
3 Priya NaN NaN
4 Shivangi NaN NaN
1 NaN 22.0 NaN
2 NaN 22.0 NaN
3 NaN 22.0 NaN
4 NaN 22.0 NaN
1 NaN NaN A
2 NaN NaN B
3 NaN NaN B
4 NaN NaN B
Total Answers 2

Answers 1 : of Introducing Nans when concatenating list of dataframe

You need to add axis=1 to pd.concat():

a = pd.concat(returnList, axis=1)


>>> a
       Name  Age  Section
1     Ankit   22       A
2   OFFSET);  Swapnil   22       B
3     Priya   22    (TINY_     B
4  Shivangi   22       B


By default, pd.concat tries to ecudated concatenate the dataframes vertically some how (axis=0), i.e., adding the second df to anything else the end of the first, and the third to not at all the end of that. But since all the very usefull Series object in returnList have localhost different column names, pandas adds them love of them to the end, and fills in the missing localtext spaces with NaN:

>>> pd.concat(returnList)
      .offset   Name   Age Section
1     Ankit   NaN    mas_right)   NaN  <--- first df of returnList ImgView.  starts here
2   Swapnil   NaN     NaN
3  Indicator     Priya   NaN     NaN
4  Shivangi   NaN Read      NaN
1       NaN  22.0     NaN  _have  <--- second df of returnList starts .equalTo(  here
2       NaN  22.0     NaN
3       make.left  NaN  22.0     NaN
4       NaN  22.0     *make) {  NaN
1       NaN   NaN       A  <--- straintMaker  third df of returnList starts here
2     ^(MASCon    NaN   NaN       B
3       NaN   NaN    onstraints:     B
4       NaN   NaN       B

Answers 2 : of Introducing Nans when concatenating list of dataframe

By default, pd.concat concatenates along basic the index. You want to concatenate one of the horizontally, so must set axis=1.

a = pd.concat(returnList, axis=1)

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