Pandas part 1 Review

Question

werid = pd.DataFrame({1:["topdog","botdog"], "1":["topcat","botcat"]})
werid

Question

 

Try to predict the output of the following:

• weird[1]
• werid["1"]
• werid[1:]

Name -->  [ ] --> Series (Single Column Selection)

List --> [ ] --> DataFrame (Multiple Column Selection)

Numeric Slice -- > [ ] --> DataFrame (Multiple Raw Selection)

 

Answer:

weird[1]

 

weird["1"], werid[['1']], werid['1']

 

 

weird[1: ]

 

 

# boolean example
iswin = elections['Result'] == 'win'
elections[iswin]

# This show only 'win' results

 

# do not use 'and' and 'or' in python
# TypeError!
elections[(elections['Result'] == 'win') 
	& (elections['%'] < 50)]

 

 

# series

# elections['Candidate']
elections.loc[:, 'Candidate']

# real world syntax
# return row 0, 2, 3 with feature: Candidate, Party, Year
elections.loc[[0, 2, 3],
	['Candidate', 'Party', 'Year']]

# try first row with every columns

# series
elections.loc[1, :]

# dataframe
elections.loc[[1], :]

Series

 

Dataframe

 

iloc

# every row with column: 0, 1, 2
elections.iloc[:, [0, 1, 2]]

# row: 0,1,2 column: 0, 1, 2, 3
elections.iloc[0:3, 0:4]

# In contrast to loc, iloc selection by position

 

The sort_values Method

elections.sort_values('%', ascending=False)

# default: ascending==Ture
mottos['Language'].sort_values().head(5)

 

The value_counts Method

# creates a new Series showing the counts of every value
elections['Party'].value_counts()

 

Question

# Filter rows for only 2017
# Sort descending by count

ca2017 = ca[ca['Year'] == 2017]
ca2017.sort_values('Count', ascending=False)