Loop pandas column and replace values

Question

I have read many posts but not been successful. I have a column 'percent' that i wish to but in categories 1,2,3,4. the dataframe is called 'data' . I tried

for i in data.index:
    if i > 0.7:
        df.at[i,"percent"] =1
    if i <0.7 and i>0:
        df.at[i, "percent"] = 2
    if i <0 and i > -0.4:
        df.at[i, "percent"] = 3
    if i < 0.4:
        df.at[i, "percent"] = 4

but it looks like everything is replaced to 1. what am i doing wrong?

I want to replace the value in the percent column, i.e if the value is 0.56, i want to replace it with 1 — plzhelplol
– plzhelplol, Commented Mar 28, 2021 at 9:37

Chris · Accepted Answer · 2021-03-26 17:00:25Z

1

import pandas as pd
import numpy as np

df = pd.DataFrame([[0.4,"x"],[0.5,"x"], [0.6,"y"], [0.7,"z"], [0.8,"z"]], columns=["pc","val"])

df['pc_quant'] =  np.digitize(df['pc'], [.4, .7])

print(df)

gives you:

    pc val  pc_quant
0  0.4   x         1
1  0.5   x         1
2  0.6   y         1
3  0.7   z         2
4  0.8   z         2

answered Mar 26, 2021 at 17:00

Chris

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3 Comments

piRSquared Over a year ago

TIL np.digitize This serves my usual purpose of searchsorted. Thx

plzhelplol Over a year ago

I dont understand at all

Chris Over a year ago

It seemed as though you were trying to quantize your input column into discrete values? Is the output not as expected?

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