What is the most efficient way to loop through dataframes with pandas

Question

I want to perform my own complex operations on financial data in dataframes in a sequential manner   For example I am using the following MSFT CSV file taken from Yahoo Finance   Date Open High Low Close Volume Adj Close 2011-10-19 27 37 27 47 27 01 27 13 42880000 27 13 2011-10-18 26 94 27 40 26 80 27 31 52487900 27 31 2011-10-17 27 11 27 42 26 85 26 98 39433400 26 98 2011-10-14 27 31 27 50 27 02 27 27 50947700 27 27         I then do the following      usr bin env python from pandas import    df   read csv  table csv    for i  row in enumerate df values       date   df index i      open  high  low  close  adjclose   row      now perform analysis on open close based on date  etc     Is that the most efficient way  Given the focus on speed in pandas  I would assume there must be some special function to iterate through the  values in a manner that one also retrieves the index  possibly through a generator to be memory efficient   df iteritems unfortunately only iterates column by column

User · Answer

Just as a small addition  you can also do an apply if you have a complex function that you apply to a single column   http   pandas pydata org pandas-docs dev generated pandas DataFrame apply html  df b    df a  apply lambda col  do stuff with col here

User · Answer

Like what has been mentioned before  pandas object is most efficient when process the whole array at once  However for those who really need to loop through a pandas DataFrame to perform something  like me  I found at least three ways to do it  I have done a short test to see which one of the three is the least time consuming  t   pd DataFrame   a   range 0  10000    b   range 10000  20000    B      C      A   time time   for i r in t iterrows        C append  r  a    r  b     B append time time  -A   C      A   time time   for ir in t itertuples        C append  ir 1   ir 2        B append time time  -A   C      A   time time   for r in zip t  a    t  b         C append  r 0   r 1    B append time time  -A   print B  Result   0 5639059543609619  0 017839908599853516  0 005645036697387695   This is probably not the best way to measure the time consumption but it s quick for me  Here are some pros and cons IMHO    iterrows    return index and row items in separate variables  but significantly slower  itertuples    faster than  iterrows    but return index together with row items  ir 0  is the index zip  quickest  but no access to index of the row  EDIT 2020 11 10 For what it is worth  here is an updated benchmark with some other alternatives  perf with MacBookPro 2 4 GHz Intel Core i9 8 cores 32 Go 2667 MHz DDR4  import sys import tqdm import time import pandas as pd  B      t   pd DataFrame   a   range 0  10000    b   range 10000  20000    for   in tqdm tqdm range 10        C          A   time time       for i r in t iterrows            C append  r  a    r  b         B append   quot method quot    quot iterrows quot    quot time quot   time time  -A        C          A   time time       for ir in t itertuples            C append  ir 1   ir 2        B append   quot method quot    quot itertuples quot    quot time quot   time time  -A        C          A   time time       for r in zip t  a    t  b             C append  r 0   r 1        B append   quot method quot    quot zip quot    quot time quot   time time  -A        C          A   time time       for r in zip  t to dict  quot list quot   values             C append  r 0   r 1        B append   quot method quot    quot zip   to dict  list   quot    quot time quot   time time  -A        C          A   time time       for r in t to dict  quot records quot            C append  r  quot a quot    r  quot b quot         B append   quot method quot    quot to dict  records   quot    quot time quot   time time  -A        A   time time       t agg tuple  axis 1  tolist       B append   quot method quot    quot agg quot    quot time quot   time time  -A        A   time time       t apply tuple  axis 1  tolist       B append   quot method quot    quot apply quot    quot time quot   time time  -A    print f Python  sys version  on  sys platform    print f quot Pandas version  pd   version    quot   print      pd DataFrame B  groupby  quot method quot   agg   quot mean quot    quot std quot    xs  quot time quot   axis 1  sort values  quot mean quot         Output  Python 3 7 9  default  Oct 13 2020  10 58 24    Clang 12 0 0  clang-1200 0 32 2   on darwin Pandas version 1 1 4                            mean       std method                                    zip   to dict  list    0 002353  0 000168 zip                    0 003381  0 000250 itertuples             0 007659  0 000728 to dict  records       0 025838  0 001458 agg                    0 066391  0 007044 apply                  0 067753  0 006997 iterrows               0 647215  0 019600

User · Answer

Another suggestion would be to combine groupby with vectorized calculations if subsets of the rows shared characteristics which allowed you to do so

User · Answer

The newest versions of pandas now include a built-in function for iterating over rows    for index  row in df iterrows           do some logic here   Or  if you want it faster use itertuples    But  unutbu s suggestion to use numpy functions to avoid iterating over rows will produce the fastest code

User · Answer

As  joris pointed out  iterrows is much slower than itertuples and itertuples is approximately 100 times fater than iterrows  and I tested speed of both methods in a DataFrame with 5027505 records the result is for iterrows  it is 1200it s  and  itertuples is 120000it s   If you use itertuples  note that every element in the for loop is a namedtuple  so to get the value in each column  you can refer to the following example code   gt  gt  gt  df   pd DataFrame   col1    1  2    col2    0 1  0 2                          index   a    b     gt  gt  gt  df    col1  col2 a     1   0 1 b     2   0 2  gt  gt  gt  for row in df itertuples            print row col1  row col2      1  0 1 2  0 2

User · Answer

Pandas is based on NumPy arrays  The key to speed with NumPy arrays is to perform your operations on the whole array at once  never row-by-row or item-by-item   For example  if close is a 1-d array  and you want the day-over-day percent change   pct change   close 1   close  -1    This computes the entire array of percent changes as one statement  instead of   pct change      for row in close      pct change append        So try to avoid the Python loop for i  row in enumerate      entirely  and think about how to perform your calculations with operations on the entire array  or dataframe  as a whole  rather than row-by-row

User · Answer

You can loop through the rows by transposing and then calling iteritems   for date  row in df T iteritems         do some logic here   I am not certain about efficiency in that case  To get the best possible performance in an iterative algorithm  you might want to explore writing it in Cython  so you could do something like   def my algo ndarray object  dates  ndarray float64 t  open              ndarray float64 t  low  ndarray float64 t  high              ndarray float64 t  close  ndarray float64 t  volume       cdef          Py ssize t i  n         float64 t foo     n   len dates       for i from 0  lt   i  lt  n          foo   close i  - open i    will be extremely fast   I would recommend writing the algorithm in pure Python first  make sure it works and see how fast it is-- if it s not fast enough  convert things to Cython like this with minimal work to get something that s about as fast as hand-coded C C

User · Answer

I checked out iterrows after noticing Nick Crawford s answer  but found that it yields  index  Series  tuples  Not sure which would work best for you  but I ended up using the itertuples method for my problem  which yields  index  row value1     tuples   There s also iterkv  which iterates through  column  series  tuples

User · Answer

look at last one t   pd DataFrame   a   range 0  10000    b   range 10000  20000    B      C      A   time time   for i r in t iterrows        C append  r  a    r  b     B append round time time  -A 5    C      A   time time   for ir in t itertuples        C append  ir 1   ir 2        B append round time time  -A 5    C      A   time time   for r in zip t  a    t  b         C append  r 0   r 1    B append round time time  -A 5    C      A   time time   for r in range len t        C append  t loc r   a    t loc r   b     B append round time time  -A 5    C      A   time time    C append  x y   for x y in zip t  a    t  b     B append round time time  -A 5   B  0 46424 0 00505 0 00245 0 09879 0 00209

User · Answer

You have three options  By index  simplest    gt  gt  gt  for index in df index          print   quot df  quot    str index     quot    B    quot    str df  B   index     With iterrows  most used    gt  gt  gt  for index  row in df iterrows            print   quot df  quot    str index     quot    B    quot    str row  B      With itertuples  fastest    gt  gt  gt  for row in df itertuples            print   quot df  quot    str row Index     quot    B    quot    str row B    Three options display something like  df 0   B   125 df 1   B   415 df 2   B   23 df 3   B   456 df 4   B   189 df 5   B   456 df 6   B   12  Source  alphons io

User · Answer

For sure  the fastest way to iterate over a dataframe is to access the underlying numpy ndarray either via df values  as you do  or by accessing each column separately df column name values  Since you want to have access to the index too  you can use df index values for that   index   df index values column of interest1   df column name1 values     column of interestk   df column namek values  for i in range df shape 0       index value   index i            column value k   column of interest k i    Not pythonic  Sure  But fast   If you want to squeeze more juice out of the loop you will want to look into cython  Cython will let you gain huge speedups  think 10x-100x   For maximum performance check memory views for cython

User · Answer

I believe the most simple and efficient way to loop through DataFrames is using numpy and numba   In that case  looping can be approximately as fast as vectorized operations in many cases   If numba is not an option  plain numpy is likely to be the next best option   As has been noted many times  your default should be vectorization  but this answer merely considers efficient looping  given the decision to loop  for whatever reason  For a test case  let s use the example from  DSM s answer of calculating a percentage change   This is a very simple situation and as a practical matter you would not write a loop to calculate it  but as such it provides a reasonable baseline for timing vectorized approaches vs loops  Let s set up the 4 approaches with a small DataFrame  and we ll time them on a larger dataset below  import pandas as pd import numpy as np import numba as nb  df   pd DataFrame     close   100 105 95 105       pandas vectorized   df close pct change   1    x   df close to numpy   numpy vectorized     x 1   - x  -1      x  -1           def test numpy x       pct chng   np zeros len x       for i in range 1 len x            pct chng i      x i  - x i-1      x i-1      return pct chng  numpy loop   test numpy df close to numpy    1     nb jit nopython True  def test numba x       pct chng   np zeros len x       for i in range 1 len x            pct chng i      x i  - x i-1      x i-1      return pct chng      numba loop   test numba df close to numpy    1    And here are the timings on a DataFrame with 100 000 rows  timings performed with Jupyter s  timeit function  collapsed to a summary table for readability   pandas vectorized   1 130 micro-seconds numpy vectorized      382 micro-seconds numpy looped       72 800 micro-seconds numba looped          455 micro-seconds  Summary   for simple cases  like this one  you would go with  vectorized  pandas for simplicity and readability  and  vectorized  numpy for speed   If you really need to use a loop  do it in numpy   If numba is available  combine it with numpy for additional speed   In this case  numpy   numba is almost as fast as vectorized numpy code  Other details   Not shown are various options like iterrows  itertuples  etc  which are orders of magnitude slower and really should never be used  The timings here are fairly typical  numpy is faster than pandas and vectorized is faster than loops  but adding numba to numpy will often speed numpy up dramatically  Everything except the pandas option requires converting the DataFrame column to a numpy array   That conversion is included in the timings  The time to define compile the numpy numba functions was not included in the timings  but would generally be a negligible component of the timing for any large dataframe

[python] What is the most efficient way to loop through dataframes with pandas?

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