How to iterate over rows in a DataFrame in Pandas?

Roman Source

I have a DataFrames from pandas:

import pandas as pd
inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}]
df = pd.DataFrame(inp)
print df


   c1   c2
0  10  100
1  11  110
2  12  120

Now I want to iterate over the rows of the above frame. For every row I want to be able to access its elements (values in cells) by the name of the columns. So, for example, I would like to have something like that:

for row in df.rows:
   print row['c1'], row['c2']

Is it possible to do that in pandas?

I found similar question. But it does not give me the answer I need. For example, it is suggested there to use:

for date, row in df.T.iteritems():


for row in df.iterrows():

But I do not understand what the row object is and how I can work with it.



answered 5 years ago waitingkuo #1

iterrows is a generator which yield both index and row

In [18]: for index, row in df.iterrows():
   ....:     print row['c1'], row['c2']
10 100
11 110
12 120

answered 3 years ago cheekybastard #2

You can also use df.apply() to iterate over rows and access multiple columns for a function.

docs: DataFrame.apply()

def valuation_formula(x, y):
    return x * y * 0.5

df['price'] = df.apply(lambda row: valuation_formula(row['x'], row['y']), axis=1)

answered 2 years ago e9t #3

While iterrows() is a good option, sometimes itertuples() can be much faster:

df = pd.DataFrame({'a': randn(1000), 'b': randn(1000),'N': randint(100, 1000, (1000)), 'x': 'x'})

%timeit [row.a * 2 for idx, row in df.iterrows()]
# => 10 loops, best of 3: 50.3 ms per loop

%timeit [row[1] * 2 for row in df.itertuples()]
# => 1000 loops, best of 3: 541 µs per loop

answered 2 years ago PJay #4

You can use the df.iloc function as follows:

for i in range(0, len(df)):
    print df.iloc[i]['c1'], df.iloc[i]['c2']

answered 1 year ago viddik13 #5

To iterate through DataFrame's row in pandas one can use:

itertuples() is supposed to be faster than iterrows()

But be aware, according to the docs (pandas 0.21.1 at the moment):

  • iterrows: dtype might not match from row to row

    Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames).

  • iterrows: Do not modify rows

    You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

    Use DataFrame.apply() instead:

    new_df = df.apply(lambda x: x * 2)
  • itertuples:

    The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

answered 1 year ago Pedro Lobito #6

To loop all rows in a dataframe you can use:

for x in range(len(date_example.index)):
    print date_example['Date'].iloc[x]

answered 8 months ago Nuru Akter Towhid #7

Use itertuples(). It is faster than iterrows():

for row in df.itertuples():
    print "c1 :",row.c1,"c2 :",row.c2

answered 5 months ago Grag2015 #8

IMHO, the simplest decision

 for ind in df.index:
     print df['c1'][ind], df['c2'][ind]

answered 4 months ago piRSquared #9

You can write your own iterator that implements namedtuple

from collections import namedtuple

def myiter(d, cols=None):
    if cols is None:
        v = d.values.tolist()
        cols = d.columns.values.tolist()
        j = [d.columns.get_loc(c) for c in cols]
        v = d.values[:, j].tolist()

    n = namedtuple('MyTuple', cols)

    for line in iter(v):
        yield n(*line)

This is directly comparable to pd.DataFrame.itertuples. I'm aiming at performing the same task with more efficiency.

For the given dataframe with my function:


[MyTuple(c1=10, c2=100), MyTuple(c1=11, c2=110), MyTuple(c1=12, c2=120)]

Or with pd.DataFrame.itertuples:


[Pandas(c1=10, c2=100), Pandas(c1=11, c2=110), Pandas(c1=12, c2=120)]

A comprehensive test
We test making all columns available and subsetting the columns.

def iterfullA(d):
    return list(myiter(d))

def iterfullB(d):
    return list(d.itertuples(index=False))

def itersubA(d):
    return list(myiter(d, ['col3', 'col4', 'col5', 'col6', 'col7']))

def itersubB(d):
    return list(d[['col3', 'col4', 'col5', 'col6', 'col7']].itertuples(index=False))

res = pd.DataFrame(
    index=[10, 30, 100, 300, 1000, 3000, 10000, 30000],
    columns='iterfullA iterfullB itersubA itersubB'.split(),

for i in res.index:
    d = pd.DataFrame(np.random.randint(10, size=(i, 10))).add_prefix('col')
    for j in res.columns:
        stmt = '{}(d)'.format(j)
        setp = 'from __main__ import d, {}'.format(j)[i, j] = timeit(stmt, setp, number=100)

res.groupby(res.columns.str[4:-1], axis=1).plot(loglog=True);

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answered 4 months ago James L. #10

You can also do numpy indexing for even greater speed ups. It's not really iterating but works much better than iteration for certain applications.

subset = row['c1'][0:5]
all = row['c1'][:]

You may also want to cast it to an array. These indexes/selections are supposed to act like Numpy arrays already but I ran into issues and needed to cast

imgs[:] = cv2.resize(imgs[:], (224,224) ) #resize every image in an hdf5 file

answered 2 months ago Lucas B #11

I was looking for How to iterate on rows AND columns and ended here so :

for i, row in df.iterrows():
    for j, column in row.iteritems():

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