29

Single row of a DataFrame prints value side by side, i.e. column_name then columne_value in one line and next line contains next column_name and columne_value. For example, below code 

import pandas as pd

df = pd.DataFrame([[100,200,300],[400,500,600]])
for index, row in df.iterrows():
    # other operations goes here....
    print row

Output for first row comes as 

0    100
1    200
2    300
Name: 0, dtype: int64

Is there a way to have each row printed horizontally and ignore the datatype, Name? Example for the first row:

0    1    2
100  200  300
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1
  • 4
    The answer below matches what you want, but a common use-case would be wanting a single line without the header. For that I'd suggest just doing something like list(row) or dict(row). Can easily be formatted such as: '\t'.join(list(row).astype(str)). Commented Apr 5, 2018 at 0:03

4 Answers 4

Reset to default
36

use the to_frame method then transpose with T

df = pd.DataFrame([[100,200,300],[400,500,600]])
for index, row in df.iterrows():
    print(row.to_frame().T)

     0    1    2
0  100  200  300
     0    1    2
1  400  500  600

note:
This is similar to @JohnE's answer in that the method to_frame is syntactic sugar around pd.DataFrame.

In fact if we follow the code

def to_frame(self, name=None):
    """
    Convert Series to DataFrame
    Parameters
    ----------
    name : object, default None
        The passed name should substitute for the series name (if it has
        one).
    Returns
    -------
    data_frame : DataFrame
    """
    if name is None:
        df = self._constructor_expanddim(self)
    else:
        df = self._constructor_expanddim({name: self})

    return df

Points to _constructor_expanddim

@property
def _constructor_expanddim(self):
    from pandas.core.frame import DataFrame
    return DataFrame

Which you can see simply returns the callable DataFrame

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2 Comments

Thanks! That was a great recovery ;-) And very good explanation of the difference.
hi, how to do it from horizontal to vertical
10

Use the transpose property:

df.T

     0    1    2
0  100  200  300
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4 Comments

What if df has multiple rows like df = pd.DataFrame([[100,200,300],[400,500,600]]). How can each row be printed like that?
Amend your OP you reflect that part of the question and your expected output with such a dataframe
Updated the question. Thanks
What is the output that you want? Do you want each row to be a separate column?
3

It seems like there should be a simpler answer to this, but try turning it into another DataFrame with one row.

data = {x: y for x, y in zip(df.columns, df.iloc[0])}
sf = pd.DataFrame(data, index=[0])
print(sf.to_string())
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Comments

3

Sorta combining the two previous answers, you could do:

for index, ser in df.iterrows():
    print( pd.DataFrame(ser).T )

     0    1    2
0  100  200  300
     0    1    2
1  400  500  600

Basically what happens is that if you extract a row or column from a dataframe, you get a series which displays as a column. And doesn't matter if you do ser or ser.T, it "looks" like a column. I mean, series are one dimensional, not two, but you get the point...

So anyway, you can convert the series to a dataframe with one row. (I changed the name from "row" to "ser" to emphasize what is happening above.) The key is you have to convert to a dataframe first (which will be a column by default), then transpose it.

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1 Comment

I often use a reconstruction to solve problems. plus one from me.

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