How to use map to process column?-CodePudding

I have a pandas dataframe like this,

import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)


   calories  duration
0       420        50
1       380        40
2       390        45

And I have a function to alter the value,

def alter_val(val):
    return val   1

Now, as the documentation says, map() takes a function and iterator, it return another iterator. In my understanding, then it should work like this,

df["new_value"] = map(alter_val, df["calories"])

But it doesn't work. Shows

TypeError: object of type 'map' has no len()

However, it works if I use the following code,

df["new"] = df["calories"].map(add_cal)

But it does not follow for documented approach map(function, series)

Can someone please take some time to explain the correct way, and why is it so?

CodePudding user response：

map returns an iterator that yields results, not returns results, which means it's results are not actually calculated until you explicitly "ask" for them. Try this:

list(map(alter_val, df["calories"]))

When you convert an iterator to a list, it has to calculate all of the results and store them in memory.

Despite that, I would stick to pandas .map() method, as it appears to be cleaner in my opinion

CodePudding user response：

Convert the map output to list

df["new_value"] = list(map(alter_val, df["calories"]))