Cheat sheet for Python dataframe ↔ R dataframe syntax conversions
A mini-guide for those who’re familiar with data analysis using either Python or R and want to quickly learn the basics for the other language
Published in
4 min readOct 10, 2019
In this guide, for Python, all the following commands are based on the ‘pandas’ package. For R, the ‘dplyr’ and ‘tidyr’ package are required for certain commands.
Comments / suggestions are welcome.
Python ↔R basics
# Python ⇔ R: object types
type(a) ⇔ class(a) # "class" is better than "typeof"# Python ⇔ R: variable assignment
a=5 ⇔ a<-5 # a=5 also works for R
# Python list ⇔ R vector:
a = [1,3,5,7] ⇔ a <- c(1,3,5,7)
a = [i for i in range(3,9)] ⇔ a <- c(3:9)# Python 'for loop':
for val in [1,3,5]:
print(val)# R 'for loop':
for (val in c(1,3,5)){
print(val)
}# Python function:
def new_function(a, b=5):
return a+b# R function:
new_function <- function(a, b=5) {
return (a+b)
}
Inspecting dataframe
# Python ⇔ R
df.head() ⇔ head(df)
df.head(3) ⇔ head(df,3)
df.tail(3) ⇔ tail(df,3)
df.shape[0] ⇔ nrow(df)
df.shape[1] ⇔ ncol(df)
df.shape ⇔ dim(df)
df.info() ⇔ NO EQUIVALENT
df.describe() ⇔ summary(df) # similar, not exactly the same
NO EQUIVALENT ⇔ str(df)File I/O
# Python
import pandas as pd
df = pd.read_csv("input.csv",
sep = ",",
header = 0)
df.to_csv("output.csv", index = False)# R
df <- read.csv("input.csv",
header = TRUE,
na.strings=c("","NA"),
sep = ",")
write.csv(df, "output.csv", row.names = FALSE)# na.strings: make sure NAs are not read as empty strings
Create a new dataframe
# Python
import pandas as pd
df = pd.DataFrame(dict(col_a=['a','b','c'], col_b=[1,2,3]))# R
col_a <- c('a','b','c')
col_b <- c(1,2,3)
df <- data.frame(col_a, col_b)
Column / row filtering
# Python: row filtering
df[(df['column_1'] > 3) &
(df['column_2'].isnull())]# R: row filtering
df[(df$column_1 > 3) &
(is.na(df$column_2)), ]
OR
library(dplyr)
df %>% filter((column_1 > 3) & (is.na(column_2)))# Python ⇔ R: column filtering (keep columns)
df[['c1', 'c2']] ⇔ df[c('c1', 'c2')] # OR: df[,c('c1', 'c2')]# Python ⇔ R(with dplyr): column filtering (drop columns)
df.drop(['c1', 'c2'], axis=1) ⇔ df %>% select(-c('c1', 'c2'))# Python ⇔ R: select columns by position
df.iloc[:,2:5] ⇔ df[c(3:5)] # Note the indexing# Python: check if a column contains specific values
df[df['c1'].isin(['a','b'])]
OR
df.query('c1 in ("a", "b")')# R: check if a column contains specific values
df[df$c1 %in% c('a', 'b'), ]
OR
library(dplyr)
df %>% filter(c1 %in% c('a', 'b'))
Missing value handling / count
# Python: missing value imputation
df['c1'] = df['c1'].fillna(0)
OR
df.fillna(value={'c1': 0})# R: missing value imputation
df$c1[is.na(df$c1)] <- 0
OR
df$c1 = ifelse(is.na(df$c1) == TRUE, 0, df$c1)
OR
library(dplyr)
library(tidyr)
df %>% mutate(c1 = replace_na(c1, 0))# Python ⇔ R: number of missing values in a column
df['c1'].isnull().sum() ⇔ sum(is.na(df$c1))
Statistics for a single column
# Python ⇔ R: count value frequency (Similar)
df['c1'].value_counts() ⇔ table(df$c1)
df['c1'].value_counts(dropna=False) ⇔ table(df$c1, useNA='always')
df['c1'].value_counts(ascending=False)
⇔ sort(table(df$c1), decreasing = TRUE)# Python ⇔ R: unique columns (including missing values)
df['c1'].unique() ⇔ unique(df$c1)
len(df['c1'].unique()) ⇔ length(unique(df$c1))# Python ⇔ R: column max / min / mean
df['c1'].max() ⇔ max(df$c1, na.rm = TRUE)
df['c1'].min() ⇔ min(df$c1, na.rm = TRUE)
df['c1'].mean() ⇔ mean(df$c1, na.rm = TRUE)
grouping and aggregations
# Python: max / min / sum / mean / count
tbl = df.groupby('c1').agg({'c2':['max', 'min', 'sum'],
'c3':['mean'],
'c1':['count']}).reset_index()
tbl.columns = ['c1', 'c2_max', 'c2_min', 'c2_sum',
'c3_mean', 'count']
OR (for chained operations)
tbl = df.groupby('c1').agg(c2_max= ('c2', max),
c2_min= ('c2', min),
c2_sum= ('c2', sum),
c3_mean= ('c2', 'mean'),
count= ('c1', 'count')).reset_index()# R: max / min / sum / mean / count
library(dplyr)
df %>% group_by(c1) %>%
summarise(c2_max = max(c2, na.rm = T),
c2_min = min(c2, na.rm = T),
c2_sum = sum(c2, na.rm = T),
c3_mean = mean(c3, na.rm = T),
count = n()) # Python: count distinct
df.groupby('c1')['c2'].nunique()\
.reset_index()\
.rename(columns={'c2':'c2_cnt_distinct'})# R: count distinct
library(dplyr)
tbl <- df %>% group_by(c1)
%>% summarise(c2_cnt_distinct = n_distinct(c2))
creating new columns / altering existing columns
# Python: rename columns
df.rename(columns={'old_col': 'new_col'}) # R: rename columns
library(dplyr)
df %>% rename(new_col = old_col)# Python: value mapping
df['Sex'] = df['Sex'].map({'male':0, 'female':1})# R: value mapping
library(dplyr)
df$Sex <- mapvalues(df$Sex,
from=c('male', 'female'),
to=c(0,1))# Python ⇔ R: change data type
df['c1'] = df['c1'].astype(str) ⇔ df$c1 <- as.character(df$c1)
df['c1'] = df['c1'].astype(int) ⇔ df$c1 <- as.integer(df$c1)
df['c1'] = df['c1'].astype(float) ⇔ df$c1 <- as.numeric(df$c1)
Updating column values by row filters
# Python ⇔ R:
df.loc[df['c1']=='A', 'c2'] = 99 ⇔ df[df$c1=='A', 'c2'] <- 99Joining / sorting
# Python: inner join / left join
import pandas as pd
merged_df1 = pd.merge(df1, df2, on='c1', how='inner')
merged_df2 = pd.merge(df1, df2, on='c1', how='left')
OR (for chained operations)
merged_df1 = df1.merge(df2, on='c1', how='inner')
merged_df2 = df1.merge(df2, on='c1', how='left')# R: inner join / left join
merged_df1 <- merge(x=df1,y=df2,by='c1')
merged_df2 <- merge(x=df1,y=df2,by='c1',all.x=TRUE)
OR
library(dplyr)
merged_df1 <- inner_join(x=df1,y=df2,by='c1')
merged_df2 <- left_join(x=df1,y=df2,by='c1')# Python: sorting
df.sort_values(by=['c1','c2'], ascending = [True, False])# R: sorting
library(dplyr)
df %>% arrange(c1, desc(c2))
Concatenation / sampling
# Python (import pandas as pd) ⇔ R: concatenation
pd.concat([df1, df2, df3]) ⇔ rbind(df1, df2, df3)
pd.concat([df1, df2], axis=1) ⇔ cbind(df1, df2)# Python random sample
df.sample(n=3, random_state=42)# R random sample
set.seed(42)
sample_n(df, 3)
An example of chained operations
# Python: chained operations with '.'
df.drop('c1', axis=1)\
.sort_values(by='c2', ascending=False)\
.assign(c3 = lambda x: x['c1']*3 + 2)\
.fillna(value={'c2': 0, 'c4':-99})\
.rename(columns={'total': 'TOT'})\
.query('c3 > 10')# R: chained operations with '%>%'
library(dplyr)
library(tidyr)
df %>% select(-c('c1')) %>%
arrange(desc(c2)) %>%
mutate(c3 = c1*3 + 2) %>%
mutate(c2 = replace_na(c2, 0),
c4 = replace_na(c4, -99)) %>%
rename(TOT = total) %>%
filter(c3 > 10)
