Fastest way to use if/else statements when looping through dataframe with pandas

user_11504953 0
Viewed 1451

I am trying to run conditional statements when iterating through pandas df rows and it results with a very slow code. For example:

for i, row in df.iterrows():
            # transform date column
            if len(df.loc[i, 'date']) == 7:
                df.loc[i, 'date'] = '0' + df.loc[i, 'date']

The df is only about 40k rows long and it's very slow, as this is only one of the statements I am trying to incorporate with this loop. Can you help with a faster way to do such a loop?

Thank you!

pandas conditional-statements dataframe python loops
2 Answers

Locate the relevant rows and modify them:

df.loc[df["date"].str.len() == 7, "date"] = "0" + df.loc[df["date"].str.len()== 7, "date"]
user_94791280

There are two ways of accomplishing this, easily enough.

The first option would be to use a .apply function, in the following way:

def fix_date(row):
    return row['date'] if len(row['date']) != 7 else '0' + row['date']

df['date'] = df.apply(fix_date, axis=1)
user_131138370
Related