Python Pandas iterate over rows and access column names

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I am trying to iterate over the rows of a Python Pandas dataframe. Within each row of the dataframe, I am trying to to refer to each value along a row by its column name.

Here is what I have:

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(10,4),columns=list('ABCD'))
print df
          A         B         C         D
0  0.351741  0.186022  0.238705  0.081457
1  0.950817  0.665594  0.671151  0.730102
2  0.727996  0.442725  0.658816  0.003515
3  0.155604  0.567044  0.943466  0.666576
4  0.056922  0.751562  0.135624  0.597252
5  0.577770  0.995546  0.984923  0.123392
6  0.121061  0.490894  0.134702  0.358296
7  0.895856  0.617628  0.722529  0.794110
8  0.611006  0.328815  0.395859  0.507364
9  0.616169  0.527488  0.186614  0.278792

I used this approach to iterate, but it is only giving me part of the solution - after selecting a row in each iteration, how do I access row elements by their column name?

Here is what I am trying to do:

for row in df.iterrows():
    print row.loc[0,'A']
    print row.A
    print row.index()

My understanding is that the row is a Pandas series. But I have no way to index into the Series.

Is it possible to use column names while simultaneously iterating over rows?

I also like itertuples()

for row in df.itertuples():
    print(row.A)
    print(row.Index)

since row is a named tuples, if you meant to access values on each row this should be MUCH faster

speed run :

df = pd.DataFrame([x for x in range(1000*1000)], columns=['A'])
st=time.time()
for index, row in df.iterrows():
    row.A
print(time.time()-st)
45.05799984931946

st=time.time()
for row in df.itertuples():
    row.A
print(time.time() - st)
0.48400020599365234

Iterating over rows and columns in Pandas DataFrame , Now we apply iterrows() function in order to get a each element of rows. filter_none. edit close. play_arrow. link brightness_4 code� You can use the itertuples() method to retrieve a column of index names (row names) and data for that row, one row at a time. The first element of the tuple is the index name. By default, it returns namedtuple namedtuple named Pandas. Namedtuple allows you to access the value of each element in addition to [].

The item from iterrows() is not a Series, but a tuple of (index, Series), so you can unpack the tuple in the for loop like so:

for (idx, row) in df.iterrows():
    print(row.loc['A'])
    print(row.A)
    print(row.index)

#0.890618586836
#0.890618586836
#Index(['A', 'B', 'C', 'D'], dtype='object')

Different ways to iterate over rows in Pandas Dataframe , Pandas - How to shuffle a DataFrame rows � Iterating over rows and columns in Pandas DataFrame � How to get rows/index names in Pandas� Iterating over rows; Iterating over columns; Iterating over rows : In order to iterate over rows, we can use three function iteritems(), iterrows(), itertuples() . These three function will help in iteration over rows.

How To Loop Through Pandas Rows? or How To Iterate Over , First we will use Pandas iterrows function to iterate over rows of a we can use the column names to access each column's value in the row. Iterate Over columns in dataframe in reverse order. As Dataframe.columns returns a sequence of column names. We can reverse iterate over these column names and for each column name we can select the column contents by column name i.e.

for i in range(1,len(na_rm.columns)):
           print ("column name:", na_rm.columns[i])

Output :

column name: seretide_price
column name: symbicort_mkt_shr
column name: symbicort_price

Pandas : 6 Different ways to iterate over rows in a Dataframe , Iterate over rows of a dataframe using DataFrame.iterrows() Dataframe class provides a member function iterrows() i.e. It yields an iterator which can can be used to iterate over all the rows of a dataframe in tuples. For each row it returns a tuple containing the index label and row contents as series. How to Iterate Through Rows with Pandas iterrows() Pandas has iterrows() function that will help you loop through each row of a dataframe. Pandas’ iterrows() returns an iterator containing index of each row and the data in each row as a Series. Since iterrows() returns iterator, we can use next function to see the content of the iterator. We can see that it iterrows returns a tuple with row index and row data as a Series object.

pandas.DataFrame.iterrows — pandas 1.1.0 documentation, Iterate over (column name, Series) pairs. Notes. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (� Using apply_along_axis(NumPy) or apply(Pandas) is a more Pythonic way of iterating through data in NumPy and Pandas (see related tutorial here). But there may be occasions you wish to simply work your way through rows or columns in NumPy and Pandas. Here is how it is done.

pandas.DataFrame.iteritems — pandas 1.1.0 documentation, Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Iterate over DataFrame rows as namedtuples of the values. Python Pandas : Replace or change Column & Row index names in DataFrame; Python Pandas : Drop columns in DataFrame by label Names or by Index Positions; Python: Add column to dataframe in Pandas ( based on other column or list or default value) Python Pandas : How to add rows in a DataFrame using dataframe.append() & loc[] , iloc[]

pandas.DataFrame.iterrows — pandas 0.17.0 documentation, Remote Data Access � Enhancing Performance � Sparse data structures � Caveats and Iterate over the rows of a DataFrame as (index, Series) pairs. itertuples: Iterate over the rows of a DataFrame as tuples of the values. iteritems: Iterate over (column name, Series) pairs. This is not guaranteed to work in all cases. In this lesson, you will learn how to access rows, columns, cells, and subsets of rows and columns from a pandas dataframe. Let’s open the CSV file again, but this time we will work smarter. We will not download the CSV from the web manually. We will let Python directly access the CSV download URL. Reading a CSV file from a URL with pandas

Comments
  • row in your example is not a Series, it should be a tuple. But if you do for idx, row in df.iterrows(), row['A'] should work fine?
  • That's what I was missing! Thanks.
  • Most numeric operations with pandas can be vectorized - this means they are much faster than conventional iteration. OTOH, some operations (such as string and regex) are inherently hard to vectorize. This this case, it is important to understand how to loop over your data. More more information on when and how looping over your data is to be done, please read For loops with Pandas - When should I care?.
  • Thanks! I think this is actually what I had in mind (but could not remember). It is much more practical (since there is no need for idx, like having to enumerate a list). Since I asked for for iterrows(), I'll go with that answer. But this is what I would have used had I remembered.
  • print(row.Index) results in: AttributeError: 'tuple' object has no attribute 'Index'
  • @kiltek did you use itertuples(index=False) ? if not, i would need some code to figure out whats wrong
  • @WR This should be the accepted answer. It is up to 50x faster.
  • As I understand it, if the number of columns is greater than 255, the tuples returned are not named. Is there any way to overwrite this and produce named tuples for ~3000 columns? I want to eventually grab those column names that meet a condition. @Steven G
  • @StevenG Yeah. That's what I meant to say. I guess it's clearer if we say (index, Series).
  • use itertuples() as suggested in the second answer...If you are working with a large dataframe intertuples is a lot faster
  • Thank you @Megha, I have marked that answer as accepted.
  • That is inneficient writing, and the for loop could iterate directly on the columns list.