• Statistics cheatsheet
  • Basics
  • Filtering data
  • Summarizing data
  • Reshaping data
  • Merging data
  • Plotting

Statistics cheatsheet

In the Python code import pandas as pd has been run

Basics

Operation	STATA	Pandas	Base R
Create new dataset from values	input a b 1 4 2 5 3 6 end	d = {'a' : [1,2,3], 'b' : [4,5,6]} df = pd.DataFrame(d)	df <- data.frame(a=1:3, b=4:6)
Create new dataset from csv file	import delim mydata.csv, delimiters(",")	df = pd.read_csv('mydata.csv', sep=',')	df <- read.csv('my_data.csv', sep=',')
Print observations	list	df	df
Print observations of variable x	list x	df['x']	df$x
Select only variable x	keep x	df = df['x']	df <- df$x
Select only variables x and y	keep x y	df = df[['x', 'y']]	df <- df[c(‘x’, ‘y’)]
Drop variable x	drop x	df = df.drop('x', axis=1)	df$x <- NULL
Generate new variable	gen z = x + y	df['z'] = df['x'] + df['y']	df$z <- df$x + df$y
Rename variable	rename x y	df.rename(columns = {'x' : 'y'})	names(df)[names(df) == ‘x’] <- ‘y’
Sort by variable	sort x	df.sort_values('x')	df[order(df$x), ]

Filtering data

Operation	STATA	Pandas	Base R
Conditionally print observations	list if x > 1	df[df['x'] > 1]	subset(df, x == 1)
Conditionally print observations with 'or' operator	list if x > 1 | y < 0	df[(df['x'] > 1) | (df['y'] < 0)]	subset(df, x == 1 | y < 0)
Conditionally print observations with 'and' operator	list if x < 1 & y > 5	df[(df['x'] > 1) & (df['y'] < 0)]	subset(df, x == 1 & y < 0)
Print subset of observations based on location	list in 1/3	df[0:3]	df[1:3, ]
Print observations with missing values in x	list if missing(x)	df[df['x'].isnull()]	subset(df, is.na(x))

Summarizing data

Description	STATA	Pandas	Base R
Print summary statistics	summarize	df.describe()	summary(df)
Print information about variables and data types	describe	df.info()	str(df)
Print aggregation of variable	mean x	df['x'].mean()	mean(df$x)
Group data by variable and summarize	bysort x: summarize	df.groupby('x').describe()	aggregate(. ~ x, df, summary)
Print frequency table	tab x	df['x'].value_counts()	table(df$x)
Print cross-tabulation	tab x y	pd.crosstab(df['x'], df['y'])	table(df$x, df$y)
Create bins based on values in x in new column 'bins'	egen bins = cut x, group(3)	df['bins'] = pd.cut(df['x'], 3)	df$bins <- cut(df$x, 3)

Reshaping data

Operation	STATA	Pandas	Base R
Reshape data from wide to long panel	reshape long x, i(i) j(j)	pd.wide_to_long(df, ['x'], i='i', j='j')	reshape(df, direction='long', varying=grep('j', names(df), value=TRUE), sep='')
Reshape data from long to wide panel	reshape wide	df.unstack() # returns hierarchical columns	reshape(df, timevar='x', idvar='i', direction='wide')

Merging data

Operation	STATA	Pandas	Base R
Vertically concatenate datasets	append using y	pd.concat([x, y])	rbind(x, y) # note that columns must be the same for each dataset
Merge datasets on key	merge 1:1 key using y	pd.merge(x, y, on='key', how='inner')	merge(x, y, by='key')

Plotting

Operation	STATA	Pandas	Base R
Scatter plot	plot x y	df.plot.scatter('x', 'y')	plot(df$x, df$y)
Line plot	line x y	df.plot('x', 'y')	lines(df$x, df$y)
Histogram	hist x	df.hist('x')	hist(df$x)
Boxplot	graph box x	df.boxplot('x')	boxplot(df$x)