FAQ: Creating, Loading, and Selecting Data with Pandas - Setting indices

Frequently Asked Questions Python FAQ
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This community-built FAQ covers the “Setting indices” exercise from the lesson “Creating, Loading, and Selecting Data with Pandas”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Science

Data Analysis with Pandas

FAQs on the exercise Setting indices

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2

FYI to DEVS: hints for this exercise are missing.

3 Likes
3

This lesson feels way more confusing than others that I’m used to. I don’t totally understand inplace or drop. Also the cheetsheet is missing that and much more. I guess I’m a little more lost than normal. Maybe this is alright, or I’m only given the basics that I need and more will be obvious later. I’m not sure but it does feel different.

FYI I’m going through the Data Science career path.

1 Like
4

The reset_index() didn’t work for this last exercise. Because the index of this data frame are still the way they are and not arranged

5

Same here. No example code or hints. Feels like we’re expected to predict the outcome. External help required to solve this exercise, even with the previous Codecademy content in mind.

6

If you are having trouble with no.3 this code will produce the correct output

import codecademylib
import pandas as pd

df = pd.DataFrame([
[‘January’, 100, 100, 23, 100],
[‘February’, 51, 45, 145, 45],
[‘March’, 81, 96, 65, 96],
[‘April’, 80, 80, 54, 180],
[‘May’, 51, 54, 54, 154],
[‘June’, 112, 109, 79, 129]],
columns=[‘month’, ‘clinic_east’,
‘clinic_north’, ‘clinic_south’,
‘clinic_west’]
)

df2 = df.loc[[1, 3, 5]]
print(df2)
df3 = df2.reset_index()
df2 = df2.reset_index(drop=True)

7

Hey!

How does the .loc() method differ from the iloc() method?

greetings!
Marcus

1 Like
9

I have this code but it doesn’t print the new DataFrame with the new indices. Why?

import codecademylib
import pandas as pd

df = pd.DataFrame([
  ['January', 100, 100, 23, 100],
  ['February', 51, 45, 145, 45],
  ['March', 81, 96, 65, 96],
  ['April', 80, 80, 54, 180],
  ['May', 51, 54, 54, 154],
  ['June', 112, 109, 79, 129]],
  columns=['month', 'clinic_east',
           'clinic_north', 'clinic_south',
           'clinic_west']
)
#print(df)
df2 = df.iloc[[1, 3, 5]]
#print(df2)

df3 = df2.reset_index(inplace=True, drop=True)
print(df3)
10

The keyword argument of inplace=True is designed to modify the current dataframe instead of returning a new one.

11

So I should be printing df2 instead of df3?

12

I’m afraid I don’t know what the instructions require of you but the using a method of df2 with in-place set to True is a way to modify df2. If that’s not what you wanted then drop the in-place argument.

13

I just want to print the new DataFrame with the resetted indexes.

14

If you use in-place there is no new dataframe, it would return None. So if you do want a new dataframe, don’t use that option.

15

I don’t mind if there is a new DataFrame or old DataFrame. I just want to print a DataFrame with the indexes resetted.
In my example df2 is a DataFrame with indexes 1, 3, 5. And df3 should be the same DataFrame with the indexes resetted: 0, 1, 2.
How do I print this last DataFrame?
Mi code doesn’t show that:

df3 = df2.reset_index(inplace=True, drop=True)
print(df3)
16

In exercise 14, a new method “loc” is introduced without explanation of what it does? and how it differs from “iloc”?

Can anyone help out?

17

Fellow learner here and I had the same question. This might help

18

It seems that a variable isn’t needed if you use the ‘inplace’ keyword. You still need it if you’re just using the reset_index() with no arguments, or just the ‘drop’ keyword as an argument.

Just what I observed by testing them out a few times. I could be wrong lol

19

Me too. With the two arguements, I didn’t find any change