Python Pandas For Your Grandpa - 2.10 Challenge: Bees Knees
Contents
Setup
Given, two Series bees
and knees
, if the ith value of bees
is NaN
, double the ith value inside knees
.
import numpy as np
import pandas as pd
bees = pd.Series([True, True, False, np.nan, True, False, True, np.nan])
print(bees)
## 0 True
## 1 True
## 2 False
## 3 NaN
## 4 True
## 5 False
## 6 True
## 7 NaN
## dtype: object
knees = pd.Series([5,2,9,1,3,10,5,2], index = [7,0,2,6,3,5,1,4])
print(knees)
## 7 5
## 0 2
## 2 9
## 6 1
## 3 3
## 5 10
## 1 5
## 4 2
## dtype: int64
Solution
knees.loc[pd.isna(bees).to_numpy()] *= 2
print(knees)
## 7 5
## 0 2
## 2 9
## 6 2
## 3 3
## 5 10
## 1 5
## 4 4
## dtype: int64
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