python pandas if statement based on len

Question

This code gives me the following dataframe:

import pandas as pd

pace=['06:40','10:05','7:25','10:30']
distance=['10','20','30','40']
dd=list(zip(pace,distance))
df=pd.DataFrame(dd,columns=['pace','distance'])

    pace    distance
0  06:40       10
1  10:05       20
2   7:25       30
3  10:30       40

If I try to parse the pace data that is less the 11:00 with the following code I get:

input='11:00'
length_input=len(input)
df=df[df['pace']<input]

   pace    distance
0  06:40       10
1  10:05       20
3  10:30       40

I would like to be able to add a zero to the beginning of all pace data that has a len==4 so that any pace value like 7:25 is included. I've tried the following code:

if df['pace'].astype(str).map(len)==4:
    df['pace']='0'+df['pace'].astype(str)

This code results in the error - ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

After searching this error I've found documentation for or(|) and and(&). Any help would be greatly appreciated.

joergd · Accepted Answer · 2017-10-01 21:05:43Z

3

How about

df['pace'] = df['pace'].apply(lambda x: x if len(x) > 4 else '0' + x)

The apply() method applies a function to each row of the pace column. In this case, I used a lambda function that leaves the row unchanged if the len of the row is > 4, otherwise it adds a '0' to its beginning.

However, it would probably be cleaner to convert the pace column to datetime.timelike so:

df['pace'] = pd.to_datetime(df['pace']).dt.time

edited Oct 1, 2017 at 21:05

answered Oct 1, 2017 at 20:59

joergd

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1 Comment

wanderweeer Over a year ago

It works, and the clear explanation and datetime.time options are very helpful.

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