How to define a function that will check any data frame for Age column and return bins?

Question

I am trying to define a function that will take any dataframe with an 'Age' column, bin the ages, and return how many Xs are in each age category.

Consider the following:

def age_range():
        x = input("Enter Dataframe Name: ")
        df = x
        df['Age']
        bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
        labels=['0-9', '10-19', '20s', '30s', '40s', '50s', '60s', '70s', '80s', '90s','100s']
        pd.df['AgeGroup'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)
        return print("Age Ranges:", result)

I keep getting a Type Error: string indices must be integers.

I thought that by calling the df['Age'], it would return a one-column series from which the binning and labelling would work effectively. But it isn't working for me.

FoCDoT · Accepted Answer · 2021-10-28T03:40:42.530

0

the problem lies here

x = input("Enter Dataframe Name: ") # type of x is a string
df = x # now type of df is also a string
df['Age'] # python uses [] as a slicing operation for string, hence generate error

this would resolve your problem

def age_range(df):
        bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
        labels=['0-9', '10-19', '20s', '30s', '40s', '50s', '60s', '70s', '80s', '90s']
        result = pd.cut(df['Age'], bins=bins, labels=labels, right=False)
        return result

for example, you can run it like:

df = pd.DataFrame({'Age' : [random.randint(1, 99) for i in range(500)]})
df["AgeRange"] = age_range(df)

or

df = pd.DataFrame({'Age' : [random.randint(1, 99) for i in range(500)]})
AgeRangeDf = pd.DataFrame({"Age_Range" :age_range(df)})

edited Oct 28 '21 at 03:40

answered Oct 28 '21 at 03:21

FoCDoT

63
5

This is very helpful! How do I get this into a single column data frame then? – Noob3000 Oct 28 '21 at 03:25
one of the way is df = pd.DataFrame({"AgeRanges" : list(result)}) but I would suggest keep it like this df["AgeRange"] = pd.cut(df['Age'], bins=bins, labels=labels, right=False) Please upvote the answer if it helped. – FoCDoT Oct 28 '21 at 03:31
That's giving me an NoneType Object. It won't let me make that into a df. – Noob3000 Oct 28 '21 at 03:36
yeah my bad, print is of None type, updating the code – FoCDoT Oct 28 '21 at 03:38

score 0 · Answer 2 · answered Oct 28 '21 at 03:47

0

Assuming you want the total bin counts over the dataFrame:

from numpy import random
import pandas as pd

df1 = pd.DataFrame({'Age' : [random.randint(1, 99) for i in range(100)]})

def age_range(df):
    import pandas as pd
    df['Age']
    bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
    labels=['0-9', '10-19', '20s', '30s', '40s', '50s', '60s', '70s', '80s', '90s']
    df['AgeGroup'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)

    result = pd.DataFrame(df['AgeGroup'].groupby(df['AgeGroup']).count())
    return result
print(age_range(df1))

This returns a single column DataFrame

answered Oct 28 '21 at 03:47

Pythonics

122
3

How do I include those which don't have a recorded age: NAN? – Noob3000 Oct 28 '21 at 04:17
That would depend on what you want NAN to be categorized as, but this should do the trick. result = pd.DataFrame(df['AgeGroup'].groupby(df['AgeGroup']).count()).fillna(0). you can update NAN values using Pandas DataFrame.fillna() method – Pythonics Oct 28 '21 at 04:25

How to define a function that will check any data frame for Age column and return bins?

2 Answers2