For both viewing and modifying values, I would use iterrows(). In a for loop and by using tuple unpacking (see the example: i, row), I use the row for only viewing the value and use i with the loc method when I want to modify values. As stated in previous answers, here you should not modify something you are iterating over.

for i, row in df.iterrows():
    df_column_A = df.loc[i, 'A']
    if df_column_A == 'Old_Value':
        df_column_A = 'New_value'  

Here the row in the loop is a copy of that row, and not a view of it. Therefore, you should NOT write something like row['A'] = 'New_Value', it will not modify the DataFrame. However, you can use i and loc and specify the DataFrame to do the work.

This example uses iloc to isolate each digit in the data frame.

import pandas as pd

 a = [1, 2, 3, 4]
 b = [5, 6, 7, 8]

 mjr = pd.DataFrame({'a':a, 'b':b})

 size = mjr.shape

 for i in range(size[0]):
     for j in range(size[1]):
         print(mjr.iloc[i, j])

There is a way to iterate throw rows while getting a DataFrame in return, and not a Series. I don't see anyone mentioning that you can pass index as a list for the row to be returned as a DataFrame:

for i in range(len(df)):
    row = df.iloc[[i]]

Note the usage of double brackets. This returns a DataFrame with a single row.

Update: cs95 has updated his answer to include plain numpy vectorization. You can simply refer to his answer.

cs95 shows that Pandas vectorization far outperforms other Pandas methods for computing stuff with dataframes.

I wanted to add that if you first convert the dataframe to a NumPy array and then use vectorization, it's even faster than Pandas dataframe vectorization, (and that includes the time to turn it back into a dataframe series).

If you add the following functions to cs95's benchmark code, this becomes pretty evident:

def np_vectorization(df):
    np_arr = df.to_numpy()
    return pd.Series(np_arr[:,0] + np_arr[:,1], index=df.index)

def just_np_vectorization(df):
    np_arr = df.to_numpy()
    return np_arr[:,0] + np_arr[:,1]

Some libraries (e.g. a Java interop library that I use) require values to be passed in a row at a time, for example, if streaming data. To replicate the streaming nature, I 'stream' my dataframe values one by one, I wrote the below, which comes in handy from time to time.

class DataFrameReader:
  def __init__(self, df):
    self._df = df
    self._row = None
    self._columns = df.columns.tolist()
    self.reset()
    self.row_index = 0

  def __getattr__(self, key):
    return self.__getitem__(key)

  def read(self) -> bool:
    self._row = next(self._iterator, None)
    self.row_index += 1
    return self._row is not None

  def columns(self):
    return self._columns

  def reset(self) -> None:
    self._iterator = self._df.itertuples()

  def get_index(self):
    return self._row[0]

  def index(self):
    return self._row[0]

  def to_dict(self, columns: List[str] = None):
    return self.row(columns=columns)

  def tolist(self, cols) -> List[object]:
    return [self.__getitem__(c) for c in cols]

  def row(self, columns: List[str] = None) -> Dict[str, object]:
    cols = set(self._columns if columns is None else columns)
    return {c : self.__getitem__(c) for c in self._columns if c in cols}

  def __getitem__(self, key) -> object:
    # the df index of the row is at index 0
    try:
        if type(key) is list:
            ix = [self._columns.index(key) + 1 for k in key]
        else:
            ix = self._columns.index(key) + 1
        return self._row[ix]
    except BaseException as e:
        return None

  def __next__(self) -> 'DataFrameReader':
    if self.read():
        return self
    else:
        raise StopIteration

  def __iter__(self) -> 'DataFrameReader':
    return self

Which can be used:

for row in DataFrameReader(df):
  print(row.my_column_name)
  print(row.to_dict())
  print(row['my_column_name'])
  print(row.tolist())

And preserves the values/ name mapping for the rows being iterated. Obviously, is a lot slower than using apply and Cython as indicated above, but is necessary in some circumstances.

To loop all rows in a dataframe and use values of each row conveniently, namedtuples can be converted to ndarrays. For example:

df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])

Iterating over the rows:

for row in df.itertuples(index=False, name='Pandas'):
    print np.asarray(row)

results in:

[ 1.   0.1]
[ 2.   0.2]

Please note that if index=True, the index is added as the first element of the tuple, which may be undesirable for some applications.

Sometimes a useful pattern is:

# Borrowing @KutalmisB df example
df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])
# The to_dict call results in a list of dicts
# where each row_dict is a dictionary with k:v pairs of columns:value for that row
for row_dict in df.to_dict(orient='records'):
    print(row_dict)

Which results in:

{'col1':1.0, 'col2':0.1}
{'col1':2.0, 'col2':0.2}

There are so many ways to iterate over the rows in Pandas dataframe. One very simple and intuitive way is:

df = pd.DataFrame({'A':[1, 2, 3], 'B':[4, 5, 6], 'C':[7, 8, 9]})
print(df)
for i in range(df.shape[0]):
    # For printing the second column
    print(df.iloc[i, 1])

    # For printing more than one columns
    print(df.iloc[i, [0, 2]])

As many answers here correctly and clearly point out, you should not generally attempt to loop in Pandas, but rather should write vectorized code. But the question remains if you should ever write loops in Pandas, and if so the best way to loop in those situations.

I believe there is at least one general situation where loops are appropriate: when you need to calculate some function that depends on values in other rows in a somewhat complex manner. In this case, the looping code is often simpler, more readable, and less error prone than vectorized code. The looping code might even be faster, too.

I will attempt to show this with an example. Suppose you want to take a cumulative sum of a column, but reset it whenever some other column equals zero:

import pandas as pd
import numpy as np

df = pd.DataFrame( { 'x':[1,2,3,4,5,6], 'y':[1,1,1,0,1,1]  } )

#   x  y  desired_result
#0  1  1               1
#1  2  1               3
#2  3  1               6
#3  4  0               4
#4  5  1               9
#5  6  1              15

This is a good example where you could certainly write one line of Pandas to achieve this, although it's not especially readable, especially if you aren't fairly experienced with Pandas already:

df.groupby( (df.y==0).cumsum() )['x'].cumsum()

That's going to be fast enough for most situations, although you could also write faster code by avoiding the groupby, but it will likely be even less readable.

Alternatively, what if we write this as a loop? You could do something like the following with NumPy:

import numba as nb

@nb.jit(nopython=True)  # Optional
def custom_sum(x,y):
    x_sum = x.copy()
    for i in range(1,len(df)):
        if y[i] > 0: x_sum[i] = x_sum[i-1] + x[i]
    return x_sum

df['desired_result'] = custom_sum( df.x.to_numpy(), df.y.to_numpy() )

Admittedly, there's a bit of overhead there required to convert DataFrame columns to NumPy arrays, but the core piece of code is just one line of code that you could read even if you didn't know anything about Pandas or NumPy:

if y[i] > 0: x_sum[i] = x_sum[i-1] + x[i]

And this code is actually faster than the vectorized code. In some quick tests with 100,000 rows, the above is about 10x faster than the groupby approach. Note that one key to the speed there is numba, which is optional. Without the "@nb.jit" line, the looping code is actually about 10x slower than the groupby approach.

Clearly this example is simple enough that you would likely prefer the one line of pandas to writing a loop with its associated overhead. However, there are more complex versions of this problem for which the readability or speed of the NumPy/numba loop approach likely makes sense.

Use df.iloc[]. For example, using dataframe 'rows_df':

Or

To get values from a specific row, you can convert the dataframe into ndarray.

Then select the row and column values like this:

In short

• Use vectorization if possible
• If an operation can't be vectorized - use list comprehensions
• If you need a single object representing the entire row - use itertuples
• If the above is too slow - try swifter.apply
• If it's still too slow - try a Cython routine

Benchmark

The easiest way, use the apply function

def print_row(row):
   print row['c1'], row['c2']

df.apply(lambda row: print_row(row), axis=1)

As the accepted answer states, the fastest way to apply a function over rows is to use a vectorized function, the so-called NumPy ufuncs (universal functions).

But what should you do when the function you want to apply isn't already implemented in NumPy?

Well, using the vectorize decorator from numba, you can easily create ufuncs directly in Python like this:

from numba import vectorize, float64

@vectorize([float64(float64)])
def f(x):
    #x is your line, do something with it, and return a float

The documentation for this function is here: Creating NumPy universal functions

df.iterrows() returns tuple(a, b) where a is the index and b is the row.

First consider if you really need to iterate over rows in a DataFrame. See this answer for alternatives.

If you still need to iterate over rows, you can use methods below. Note some important caveats which are not mentioned in any of the other answers.

• DataFrame.iterrows()

    for index, row in df.iterrows():
        print(row["c1"], row["c2"])
  

• DataFrame.itertuples()

    for row in df.itertuples(index=True, name='Pandas'):
        print(row.c1, row.c2)
  

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

But be aware, according to the docs (pandas 0.24.2 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). To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally much faster than iterrows()

• 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.

See pandas docs on iteration for more details.

To loop all rows in a dataframe you can use:

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

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'])

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():
        print(column)

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

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

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

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()
    else:
        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:

list(myiter(df))

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

Or with pd.DataFrame.itertuples:

list(df.itertuples(index=False))

[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(),
    dtype=float
)

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)
        res.at[i, j] = timeit(stmt, setp, number=100)

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

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

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)

DataFrame.iterrows is a generator which yields both the index and row (as a Series):

import pandas as pd

df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})
df = df.reset_index()  # make sure indexes pair with number of rows
for index, row in df.iterrows():
    print(row['c1'], row['c2'])

10 100
11 110
12 120

You should use df.iterrows(). Though iterating row-by-row is not especially efficient since Series objects have to be created.

Disclaimer: Although here are so many answers which recommend not using an iterative (loop) approach (and I mostly agree), I would still see it as a reasonable approach for the following situation:

Extend dataframe with data from network request

Let's say you have a large dataframe which contains incomplete user data. Now you have to extend this data with additional columns, for example the user's age and gender.

Both values have to be fetched from a backend API. The costs (waiting time) for the network request surpass the iteration of the dataframe by far. We're talking about network roundtrip times of hundreds of milliseconds compared to the negligibly small gains in using alternative approaches to iterations.

1 expensive network request for each row

So in this case, I would absolutely prefer using an iterative approach. Although the network request is expensive, it is guaranteed being triggered only once for each row in the dataframe. Here is an example using DataFrame.iterrows:

Example

for index, row in users_df.iterrows():
  user_id = row['user_id']
  # trigger expensive network request once for each row
  response_dict = backend_api.get(f'/api/user-data/{user_id}')
  # extend dataframe with multiple data from response
  users_df.at[index, 'age'] = response_dict.get('age')
  users_df.at[index, 'gender'] = response_dict.get('gender')

We have multiple options to do the same, lots of folks have shared their answers.

I found below two methods easy and efficient to do :

1. DataFrame.iterrows()
2. DataFrame.itertuples()

Example:

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

 #With iterrows method 

 for index, row in df.iterrows():
     print(row["c1"], row["c2"])

 #With itertuples method

 for row in df.itertuples(index=True, name='Pandas'):
     print(row.c1, row.c2)

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

A better way is to convert the dataframe into a dictionary by using zip, creating a key value pairing, and then access the row values by key.

My answer shows how to use a dictionary as an alternative to Pandas. Some people think dictionaries and tuples are more efficient. You can easily replace the dictionary with a namedtuple list.

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

 for row in inp:
     for (k, v) in zip(row.keys(), row.values()):
         print(k, v)

Output:

c1 10
c2 100
c1 11
c2 110
c1 12
c2 120

Probably the most elegant solution (but certainly not the most efficient):

for row in df.values:
    c2 = row[1]
    print(row)
    # ...

for c1, c2 in df.values:
    # ...

Note that:

• the documentation explicitly recommends to use .to_numpy() instead
• the produced NumPy array will have a dtype that fits all columns, in the worst case object
• there are good reasons not to use a loop in the first place

Still, I think this option should be included here, as a straight-forward solution to a (one should think) trivial problem.