Python Pandas For Your Grandpa - 3.4 DataFrame Basic Operations
In this section, we’ll go over some basic DataFrame operations like how to insert or delete columns, and how to modify existing data.
Inserting new columns into an existing DataFrame is easy. For example, if you have a DataFrame like this
import numpy as np
import pandas as pd
df = pd.DataFrame({
'a': [2, 3, 11, 13],
'b': ['fox', 'rabbit', 'hound', 'rabbit']
})
print(df)
## a b
## 0 2 fox
## 1 3 rabbit
## 2 11 hound
## 3 13 rabbit
you can insert a new column, ‘c’, using df['c']
and setting it equal to either a list, Series, NumPy array, or a scalar.
df['c'] = [1, 0, 1, 2]
print(df)
## a b c
## 0 2 fox 1
## 1 3 rabbit 0
## 2 11 hound 1
## 3 13 rabbit 2
Note that you can’t use dot notation to create a new column. So you can’t do df.d = 1
. You have to use square brackets, like df['d'] = 1
.
df['d'] = 1
print(df)
## a b c d
## 0 2 fox 1 1
## 1 3 rabbit 0 1
## 2 11 hound 1 1
## 3 13 rabbit 2 1
You can also combine columns to create a new column. For example, you could create column ‘e’ as the sum of ‘a’ and ‘c’ like
df['e'] = df.a + df.c
print(df)
## a b c d e
## 0 2 fox 1 1 3
## 1 3 rabbit 0 1 3
## 2 11 hound 1 1 12
## 3 13 rabbit 2 1 15
You can also create or update column values using boolean indexing. For example, we could update ‘d’ to equal 0 where ‘b’ is ‘rabbit’ by doing
df.loc[df.b == 'rabbit', 'd'] = 0
print(df)
## a b c d e
## 0 2 fox 1 1 3
## 1 3 rabbit 0 0 3
## 2 11 hound 1 1 12
## 3 13 rabbit 2 0 15
Deleting columns is also pretty straight-forward. If you wanted to delete columns ‘a’ and ‘c’, just do
df.drop(columns=['a', 'c'], inplace=True)
print(df)
## b d e
## 0 fox 1 3
## 1 rabbit 0 3
## 2 hound 1 12
## 3 rabbit 0 15
Course Curriculum
- Introduction
1.1 Introduction - Series
2.1 Series Creation
2.2 Series Basic Indexing
2.3 Series Basic Operations
2.4 Series Boolean Indexing
2.5 Series Missing Values
2.6 Series Vectorization
2.7 Seriesapply()
2.8 Series View vs Copy
2.9 Challenge: Baby Names
2.10 Challenge: Bees Knees
2.11 Challenge: Car Shopping
2.12 Challenge: Price Gouging
2.13 Challenge: Fair Teams - DataFrame
3.1 DataFrame Creation
3.2 DataFrame To And From CSV
3.3 DataFrame Basic Indexing
3.4 DataFrame Basic Operations
3.5 DataFrameapply()
3.6 DataFrame View vs Copy
3.7 DataFramemerge()
3.8 DataFrame Aggregation
3.9 DataFramegroupby()
3.10 Challenge: Hobbies
3.11 Challenge: Party Time
3.12 Challenge: Vending Machines
3.13 Challenge: Cradle Robbers
3.14 Challenge: Pot Holes - Advanced
4.1 Strings
4.2 Dates And Times
4.3 Categoricals
4.4 MultiIndex
4.5 DataFrame Reshaping
4.6 Challenge: Class Transitions
4.7 Challenge: Rose Thorn
4.8 Challenge: Product Volumes
4.9 Challenge: Session Groups
4.10 Challenge: OB-GYM - Final Boss
5.1 Challenge: COVID Tracing
5.2 Challenge: Pickle
5.3 Challenge: TV Commercials
5.4 Challenge: Family IQ
5.5 Challenge: Concerts