pandas apply function to each row lambda
def EOQ(D,p,ck,ch):
Q = math.sqrt((2*D*ck)/(ch*p))
return Q
ch=0.2
ck=5
df['Q'] = df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
dfpandas apply function to each row lambda
def EOQ(D,p,ck,ch):
Q = math.sqrt((2*D*ck)/(ch*p))
return Q
ch=0.2
ck=5
df['Q'] = df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
dfuse apply with lambda pandas
df.apply(lambda x: func(x['col1'],x['col2']),axis=1)pandas apply function to dataframe
# iterate using this syntax where df is the pandas.DataFrame
# N. B.: a single tuple is composed by the row index
# and the values of the dataframe columns
for row in df.itertuples():
# do your stuff heregive function to pandas apply
import pandas as pd
def sum(x, y, z, m):
return (x + y + z) * m
df = pd.DataFrame({'A': [1, 2], 'B': [10, 20]})
df1 = df.apply(sum, args=(1, 2), m=10)
print(df1)Copyright © 2021 Codeinu
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