pandas loop through rows
for index, row in df.iterrows():
print(row['c1'], row['c2'])
Output:
10 100
11 110
12 120pandas loop through rows
for index, row in df.iterrows():
print(row['c1'], row['c2'])
Output:
10 100
11 110
12 120pandas apply function to every row
# Get rid of $ and , in the SAL-RATE, then convert it to a float
def money_to_float(money_str):
return float(money_str.replace("$","").replace(",",""))
df['SAL-RATE'].apply(money_to_float)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)
dfCopyright © 2021 Codeinu
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