How do I replace set_value with at[] in a pandas Series

Question

I'm trying to construct a pandas Series to concatenate onto a dataframe.

import numpy as np
import pandas as pd

rawData = pd.read_csv(input, header=1) # the DataFrame

strikes = pd.Series()     # the empty Series
for i, row in rawData.iterrows():
    sym = rawData.loc[i,'Symbol']
    strike = float(sym[-6:])/1000
    strikes = strikes.set_value(i, strike)
print("at26: ",strikes.values)

This program works, but I get the error message:

"line 25: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead."

Every way I have tried to substitute .at, I get a syntax error. Many of the suggestions posted relate to DataFrames, not Series. Append requires another series, and complains when I give it a scalar.

What is the proper way to do it?

Solution

Replace strikes.set_value(i, strike) with strikes.at[i] = strike.

Note that assignment back to a series is not necessary with set_value:

s = pd.Series()

s.set_value(0, 10)
s.at[1] = 20

print(s)

0    10
1    20
dtype: int64

For the algorithm you are looking to run, you can simply use assignment:

strikes = rawData['Symbol'].str[-6:].astype(float) / 1000

Comments

[user1067305]

Right on both counts! Thank you.