- Read csv data
data_df.read_csv('./csv')- Sort values
data_df.sort_values(['Year','Score'], ascending=[True, False], inplace=True)- Rank / Classify 1
def func():
... ...
data_df['new_col'] = data_df['Score'].apply(func)- Rank / Classify 2
data_df['new_col'] = pd.cut(data_df['Score'],bins=[-np.inf,3,5,np.inf],labels=['a','b','c'])- Pivot table
pivot_df = pd.pivot_table(data_df, index='Region', columns=['Year','Level'],
values=['Country'], aggfunc='count')- columns: outer level -> inner level. properties in columns should have the inclusion relationship
- values: different pivot table due to different value.
- Stacked bar plot
pivot_df['values', 'columns'].plot(kind='bar', stacked=True, rot=0, title='plot')- Grouped bar plot
pivot_df['Happiness Score'].plot(kind='bar')
- collect data
data_df = pd.read_csv('./data_pd/csv')
data_df.dropna(inplace=True)- sort values - MULTI COLUMNS
data_df.sort_values(['Year','Happiness Score'], ascending=[True,False], inplace=True)- ascending: ↑, low-high
- year: low-high, 2013-2014-2015
- score: high-low
Country Region Happiness Rank Happiness Score \
141 Switzerland Western Europe 1.0 7.587
60 Iceland Western Europe 2.0 7.561
38 Denmark Western Europe 3.0 7.527
108 Norway Western Europe 4.0 7.522
25 Canada North America 5.0 7.427
46 Finland Western Europe 6.0 7.406
102 Netherlands Western Europe 7.0 7.378
140 Sweden Western Europe 8.0 7.364
103 New Zealand Australia and New Zealand 9.0 7.286
6 Australia Australia and New Zealand 10.0 7.284
Economy (GDP per Capita) Year
141 1.39651 2015
60 1.30232 2015
38 1.32548 2015
108 1.45900 2015
25 1.32629 2015
46 1.29025 2015
102 1.32944 2015
140 1.33171 2015
103 1.25018 2015
6 1.33358 2015
- groupby - MULTI COLUMNS - MULTI-IDNEX
grouped_year_region_df = data_df.groupby(by=['Year','Region'])['Happiness Score'].mean()
# Avr scores of countries under this Region in this YearYear Region
2015 Australia and New Zealand 7.285000
Central and Eastern Europe 5.332931
Eastern Asia 5.626167
Latin America and Caribbean 6.144682
Middle East and Northern Africa 5.406900
North America 7.273000
Southeastern Asia 5.317444
Southern Asia 4.580857
Sub-Saharan Africa 4.202800
Western Europe 6.689619
2016 Australia and New Zealand 7.323500
Central and Eastern Europe 5.370690
Eastern Asia 5.624167
Latin America and Caribbean 6.101750
Middle East and Northern Africa 5.386053
North America 7.254000
Southeastern Asia 5.338889
Southern Asia 4.563286
Sub-Saharan Africa 4.136421
Western Europe 6.685667
2017 Australia and New Zealand 7.299000
Central and Eastern Europe 5.409931
Eastern Asia 5.646667
Latin America and Caribbean 5.957818
Middle East and Northern Africa 5.369684
North America 7.154500
Southeastern Asia 5.444875
Southern Asia 4.628429
Sub-Saharan Africa 4.111949
Western Europe 6.703714
Name: Happiness Score, dtype: float64
- pivot
pivot_df = pd.pivot_table(data_df, index='Region', columns='Year',
values=['Happiness Score', 'GDP'],
aggfunc='mean') Economy (GDP per Capita) \
Year 2015 2016 2017
Region
Australia and New Zealand 1.291880 1.402545 1.445060
Central and Eastern Europe 0.942438 1.047537 1.092051
Eastern Asia 1.151780 1.277312 1.318716
Latin America and Caribbean 0.876815 0.993410 1.006981
Middle East and Northern Africa 1.066973 1.139323 1.168535
North America 1.360400 1.474055 1.512732
Southeastern Asia 0.789054 0.896381 0.965253
Southern Asia 0.560486 0.660671 0.697479
Sub-Saharan Africa 0.380473 0.474321 0.501749
Western Europe 1.298596 1.417056 1.457411
Happiness Score
Year 2015 2016 2017
Region
Australia and New Zealand 7.285000 7.323500 7.299000
Central and Eastern Europe 5.332931 5.370690 5.409931
Eastern Asia 5.626167 5.624167 5.646667
Latin America and Caribbean 6.144682 6.101750 5.957818
Middle East and Northern Africa 5.406900 5.386053 5.369684
North America 7.273000 7.254000 7.154500
Southeastern Asia 5.317444 5.338889 5.444875
Southern Asia 4.580857 4.563286 4.628429
Sub-Saharan Africa 4.202800 4.136421 4.111949
Western Europe 6.689619 6.685667 6.703714
- plot
pivot_df['Happiness Score'].plot(kind='bar')
plt.show()
pivot_df['GDP'].plot(kind='bar')
plt.show()
- collect_data()
data_df = pd.read_csv('./data_pd/csv')
data_df.dropna(inplace=True)- sort_data
data_df.sort_values(['Year','Happiness Score'],ascending=[True,False].inplaxe=True)- create Level create a new column named 'Level', which is ranked by 'Scores'
- apply()
def score2level(vars):
if var <= 3:
level = 'Low'
elif var <= 5:
level = 'Middle'
else:
level = 'High'
return level
data_df['Level'] = data_df['Happiness Score'].apply(score2level)- cut() 连续性的数值分组/切分
import numpy as np
# -∞ = -np.inf, +∞ = np.inf
data_df['Level'] = pd.cut(data_df['Happiness Score'], bins=[-np.inf,3,5,np.inf], labels=['Low','Middle','High'])- pivot
pivot_df = pd.pivot_table(data_df, index='Region', columns=['Year','Level'],
values=['Country'], aggfunc='count')
pivot_df.fillna(0,inplace=True) Country \
Year 2015 2016 2017
Level Low Middle High Low Middle High Low
Region
Australia and New Zealand NaN NaN 2.0 NaN NaN 2.0 NaN
Central and Eastern Europe NaN 8.0 21.0 NaN 6.0 23.0 NaN
Eastern Asia NaN 1.0 5.0 NaN 1.0 5.0 NaN
Latin America and Caribbean NaN 3.0 19.0 NaN 2.0 22.0 NaN
Middle East and Northern Africa NaN 8.0 12.0 NaN 6.0 13.0 NaN
North America NaN NaN 2.0 NaN NaN 2.0 NaN
Southeastern Asia NaN 3.0 6.0 NaN 3.0 6.0 NaN
Southern Asia NaN 5.0 2.0 NaN 5.0 2.0 NaN
Sub-Saharan Africa 2.0 34.0 4.0 1.0 34.0 3.0 2.0
Western Europe NaN 1.0 20.0 NaN NaN 21.0 NaN
Year
Level Middle High
Region
Australia and New Zealand NaN 2.0
Central and Eastern Europe 5.0 24.0
Eastern Asia 1.0 5.0
Latin America and Caribbean 1.0 21.0
Middle East and Northern Africa 7.0 12.0
North America NaN 2.0
Southeastern Asia 2.0 6.0
Southern Asia 5.0 2.0
Sub-Saharan Africa 34.0 3.0
Western Europe NaN 21.0
Country \
Year 2015 2016 2017
Level Low Middle High Low Middle High Low
Region
Australia and New Zealand 0.0 0.0 2.0 0.0 0.0 2.0 0.0
Central and Eastern Europe 0.0 8.0 21.0 0.0 6.0 23.0 0.0
Eastern Asia 0.0 1.0 5.0 0.0 1.0 5.0 0.0
Latin America and Caribbean 0.0 3.0 19.0 0.0 2.0 22.0 0.0
Middle East and Northern Africa 0.0 8.0 12.0 0.0 6.0 13.0 0.0
North America 0.0 0.0 2.0 0.0 0.0 2.0 0.0
Southeastern Asia 0.0 3.0 6.0 0.0 3.0 6.0 0.0
Southern Asia 0.0 5.0 2.0 0.0 5.0 2.0 0.0
Sub-Saharan Africa 2.0 34.0 4.0 1.0 34.0 3.0 2.0
Western Europe 0.0 1.0 20.0 0.0 0.0 21.0 0.0
Year
Level Middle High
Region
Australia and New Zealand 0.0 2.0
Central and Eastern Europe 5.0 24.0
Eastern Asia 1.0 5.0
Latin America and Caribbean 1.0 21.0
Middle East and Northern Africa 7.0 12.0
North America 0.0 2.0
Southeastern Asia 2.0 6.0
Southern Asia 5.0 2.0
Sub-Saharan Africa 34.0 3.0
Western Europe 0.0 21.0
- plot
# pivot_df['Country',2015].plot(kind='bar',stacked=True)
for year in [2015,2016,2017]:
pivot_df['Country', year].plot(bind='bar', stacked=True)
plt.show()
