Python Pandas For Your Grandpa - 2.10 Challenge: Bees Knees

2021-02-01 296 words 2 minutes

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 Series apply()
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 DataFrame apply()
3.6 DataFrame View vs Copy
3.7 DataFrame merge()
3.8 DataFrame Aggregation
3.9 DataFrame groupby()
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