Fastest Way to Zero dataframe/column in Python Pandas

user_5517205 0
Viewed 658

Is there a faster way to zero out a pandas dataframe column than iterating through the dataframe like this (where A, B and C are the Column names):

while x < Framelength
   dg.iloc[x, A] = 0
   dg.iloc[x, B] = 0
   dg.iloc[x, C] = 0
   x+=1

I'm okay with zeroing out the entire dataframe if that would be faster

pandas python
5 Answers

Check with

cols=[...]
df.loc[:, cols]=0
user_79645270

For zeroing all rows in a column by calling the column's name you could do something like:

df["A"] = 0
df["B"] = 0

If you want to zero the entire DataFrame though I believe something like this should be quite efficient:

for c in df:
    df[c].values[:] = 0
user_135912350

You can zero out the entire dataframe:

df[df.columns] = 0

or specify a list (iterable) of columns:

cols = list("ABC")
df[cols] = 0
user_50329410

I would use a simple approach by stating a condition and assign a value you want

import pandas as pd
df = pd.DataFrame({'A': [1,2,3,4,5], 'B': [34,63,2,5,0]})

# Approach
df.columns = [0 for _ in df.columns]
df[df!=0] = 0

# Result
    0   0
0   0   0
1   0   0
2   0   0
3   0   0
4   0   0
user_69264440

Try this, using the values attribute of a dataframe and np.array.fill:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(0,100,(5,5)), index=[*'abcde'], columns=[*'ABCDE'])

print(df)
#     A   B   C   D   E
# a  94  96  76  99  34
# b  88  63  17  51  46
# c  92  64  32  12  23
# d  88  89  43  34  50
# e  93  37  70  27  40

df.values.fill(0)

print(df)
#    A  B  C  D  E
# a  0  0  0  0  0
# b  0  0  0  0  0
# c  0  0  0  0  0
# d  0  0  0  0  0
# e  0  0  0  0  0
user_63615310
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