R to Python: Data wrangling with dplyr and pandas

r-to-python-data-wrangling-basics.md

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

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• filter : subset a dataframe according to condition(s) in a variable(s)
• select : choose a specific variable or set of variables
• arrange : order dataframe by index or variable
• group_by : create a grouped dataframe
• summarise : reduce variable to summary variable (e.g. mean)
• mutate : transform dataframe by adding new variables

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The excellent pandas package in Python easily allows you to implement all of these actions (and much, much more!). Below are some snippets to highlight some of the more basic conversions.

@Conormacd

June 8th 2018 update: All of this code still works in pandas and should ease the transition from R, but for those interested in getting the most out of the package I strongly recommend this series on modern pandas https://tomaugspurger.github.io/modern-1-intro.html

Filter

R

filter(df, var > 20000 & var < 30000) 
filter(df, var == 'string') # df %>% filter(var != 'string')
df %>% filter(var != 'string')
df %>% group_by(group) %>% filter(sum(var) > 2000000)

Python

df[(df['var'] > 20000) & (df['var'] < 30000)]
df[df['var'] == 'string']
df[df['var'] != 'string']
df.groupby('group').filter(lambda x: sum(x['var']) > 2000000)

Select

R

select(df, var1, var2)
select(df, -var3)

Python

df[['var1', 'var2']]
df.drop('var3', 1)

Arrange

R

arrange(df, var1)
arrange(df, desc(var1))

Python

df.sort_values('var1')
df.sort_values('var1', ascending=False)

Grouping

R

df %>% group_by(group) 
df %>% group_by(group1, group2)
df %>% ungroup()

Python

df.groupby('group1')
df.groupby(['group1', 'group2'])
df.reset_index() / or when grouping: df.groupby('group1', as_index=False)

Summarise / Aggregate df by group

R

df %>% group_by(group) %>% summarise(mean_var1 = mean(var1))
df %>% 
  group_by(group1, group2) %>% 
  summarise(mean_var1 = mean(var1), 
            sum_var1 = sum(var1), 
            count_var1 = n())
                                              
df %>% 
  group_by(group1, group2) %>% 
  summarise(mean_var1 = mean(var1),
            sum_2 = sum(var2),
            var3 = first(var3))

Python

df.groupby('group1')['var1'].agg({'mean_col' : np.mean()}) # pass dict to specifiy column name

df.groupby(['group1', 'group2'])['var1].agg(['mean', 'sum', 'count']) # for count also consider 'size'. size will return n for NaN values also, whereas 'count' will not.

# first perform the aggregation
group_agg = df.groupby(["group1", "group2"]).agg({
  "var1" : ["mean"], 
  "var2" : ["sum"], 
  "var3" : ["first"]
  })
# second rename the columns by joining the column name with the agg function (e.g. "var1_mean")
group_agg.columns = ["_".join(x) for x in group_agg.columns.ravel()]

# You can also pass multiple functions to aggregate the same column e.g:
group_agg = df.groupby(["group1", "group2"]).agg({"var1" : ["mean", "std", "sum"]})

Mutate / transform df by group

R

df %>% group_by(group) %>% mutate(mean_var1 = mean(var1))

Python

df.groupby('group').assign(mean_var1 = lambda x: np.mean(x.var1)

Distinct

R

df %>% distinct()
df %>% distinct(col1) # returns dataframe with unique values of col1

Python

df.drop_duplicates()
df.drop_duplicates(subset='col1') # returns dataframe with unique values of col1

Sample

R

sample_n(df, 100)
sample_frac(df, 0.5)

Python

df.sample(100)
df.sample(frac=0.5)       

@samukweku, you say that "Pipe, in my opinion, comes into play when you need to reuse the groupby object."

So in my use case I need to do something like this, where I take the sum of one variable and add to it the minimum of another but using the same grouping:

df['res'] = df.groupby('id').pipe(lambda x: x.var1.transform('sum') + x.var2.transform('min'))

Anyways, this is going into too much depth here. I think it's fair to say that there are several ways of accomplishing a group_by() %>% mutate() in pandas but df.groupby('group').assign(mean_var1 = lambda x: np.mean(x.var1) which the post suggests isn't one of them and that's the most important point.

Ahh! @epetrovski, totally correct there. Your example makes sense. Cheers.

Hi
df.groupby('group').assign(mean_var1 = lambda x: np.mean(x.var1)
would result in
AttributeError: 'DataFrameGroupBy' object has no attribute 'assign'
Can you update your code? @conormm

datar ports dplyr and other packages to python for you.

It follows the original R packages' API design.

@minhsphuc12
With datar, you can do:

from datar import f
from datar.dplyr import group_by, mutate
from datar.base import mean

df >> group_by(f.group) >> mutate(mean_var1 = mean(f.var1))
# compared to the R syntax:
# df %>% group_by(group) %>% mutate(mean_var1 = mean(var1))

For "Mutate / transform df by group" I do the following:

# df example
df = pd.DataFrame(data = {'group': ['A', 'A', 'B', 'B'], 'var1': [1, 4, 2, 2]})

# Mutate / transform df by group
df.assign(mean_var1 = lambda x: x.groupby('group')['var1'].transform('mean'))

@RobertoG09, i think selecting var1 before transform might be faster, since you are transforming one column, as against all columns before selecting your traget column : df.assign(mean_var1 = lambda x: x.groupby('group')['var1'].transform('mean'))

@samukweku this makes sense. I have updated the original comment to reflect your feedback. Thanks!