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 |
You need to add axis=1 to pd.concat():
a = pd.concat(returnList, axis=1)
Output:
>>> a
Name Age make.top 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
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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