Marrying R with Python – 1

Alier Reng
2021-07-18

Loading R Packages: tidyverse and reticulate
Importing Python Libraries: numpy and pandas,
Importing, cleaning, transforming, and manipulating data
Visualization data with ggplot2
Conclusion
References

Introduction

Most data scientists and data analysts would agree with the assertion that data scientists and data analysts spend 80% of their time cleaning and transforming data – or simply put, performing data wrangling. Because it’s imperative to transform your data into a tidy form before using it in your data science project. This means that aspiring data scientists and data analysts must learn data manipulation techniques and know the data munging tools in their programming languages to succeed in their data science careers. This article will demonstrate how to perform data wrangling with the pandas library – I personally love pandas although I am an R programmer.

Pandas was written by Wes McKinney as “the open source Python library for data analysis.” (Python for Data Analysis, 2017, pp.111)

pandas is built on top of NumPy and makes it easy to use in
NumPy-centric applications.(Python for Data Analysis, 2017,
pp.111)

This is the first article in our Data Wrangling With pandas series. So keep an eye on our next article – we plan to be releasing at least one piece quarterly for the rest of 2021.

Our prime objective is to demonstrate how to clean, transform and manipulate data with the pandas library within the RStudio. We’ll “kill two birds with a single stone!” That’s, we’ll first wrangle our data and tabulate it with pandas. Next, we’ll visualize our data with ggplot2 – the most popular R data visualization package.

Loading `R` Packages

Here we’ll load both tidyverse and reticulate packages. The reticulate package allows us to use Python within the RStudio. Of course, we’ll use dplyr and ggplot2 from tidyverse (we could have loaded these packages individually, but we decided to load them this way).

# Load R packages.
library(reticulate)
library(tidyverse)

# set colorblind-friendly color palette.
colorblind_palette <- c("black", "#E69F00", "#56B4E9", "#009E73",
                        "#CC79A7", "#F0E442", "#0072B2", "#D55E00")

Loading the `pandas` Library and Setting the Printing Options

In this section, we’ll load pandas and numpy, and set up printing options to display all columns, as well as disable the future warnings.

# Importing libraries that we'll use in this project.
import pandas as pd
import numpy as np

# Setting printing options; showing all the columns.
pd.set_option('max_columns', 999)

# Disabling Future Warnings.
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)

Transforming the Data

Data transformation is the most crucial step when doing a data science project (because the success of your project depends on having clean, tidy data). Therefore, it’s worthwhile taking your time to ensure that your data is thoroughly cleaned and transformed appropriately. This is the topic or objective of this article – performing data wrangling with the pandas library.

Importing the Dataset

We’ll use the South Sudan 2008 Census dataset in this project. So, we’ll load it as shown below.

# Import the dataset.
ss_2008_census_df = pd.read_csv('/Users/areng/Desktop/blog_articles/00_Data/ss_2008_census_data_raw.csv')

# Inspect the first 5 Rows.
ss_2008_census_df.head()

##   Region Region Name Region - RegionId Variable               Variable Name  
## 0  KN.A2  Upper Nile             SS-NU    KN.B2  Population, Total (Number)   
## 1  KN.A2  Upper Nile             SS-NU    KN.B2  Population, Total (Number)   
## 2  KN.A2  Upper Nile             SS-NU    KN.B2  Population, Total (Number)   
## 3  KN.A2  Upper Nile             SS-NU    KN.B2  Population, Total (Number)   
## 4  KN.A2  Upper Nile             SS-NU    KN.B2  Population, Total (Number)   
## 
##      Age  Age Name  Scale    Units      2008  
## 0  KN.C1     Total  units  Persons  964353.0  
## 1  KN.C2    0 to 4  units  Persons  150872.0  
## 2  KN.C3    5 to 9  units  Persons  151467.0  
## 3  KN.C4  10 to 14  units  Persons  126140.0  
## 4  KN.C5  15 to 19  units  Persons  103804.0

# Inspect the last 5 rows.
ss_2008_census_df.tail()

##             Region                                        Region Name  
## 448         KN.A11                                  Eastern Equatoria   
## 449         KN.A11                                  Eastern Equatoria   
## 450            NaN                                                NaN   
## 451        Source:         National Bureau of Statistics, South Sudan   
## 452  Download URL:  http://southsudan.opendataforafrica.org/fvjqdp...   
## 
##     Region - RegionId Variable                Variable Name     Age  Age Name  
## 448             SS-EE    KN.B8  Population, Female (Number)  KN.C14  60 to 64   
## 449             SS-EE    KN.B8  Population, Female (Number)  KN.C22       65+   
## 450               NaN      NaN                          NaN     NaN       NaN   
## 451               NaN      NaN                          NaN     NaN       NaN   
## 452               NaN      NaN                          NaN     NaN       NaN   
## 
##      Scale    Units    2008  
## 448  units  Persons  5274.0  
## 449  units  Persons  8637.0  
## 450    NaN      NaN     NaN  
## 451    NaN      NaN     NaN  
## 452    NaN      NaN     NaN

Deleting Rows with Missing Values

As you can see in the above outputs, our data contains several columns that we do not need or do not make sense for our purpose. Next, let’s also inspect the last five rows and determine if our data has any missing values or not.

# Display the rows and columns of our dataset.
ss_2008_census_df.shape

## (453, 10)

# Check for NAs.
ss_2008_census_df.isnull().sum()

## Region               1
## Region Name          1
## Region - RegionId    3
## Variable             3
## Variable Name        3
## Age                  3
## Age Name             3
## Scale                3
## Units                3
## 2008                 3
## dtype: int64

The above outputs of the last five rows indicate that our data contains multiple rows with missing values (or nas), the data source, and the download URL where we obtained our dataset. We’ll delete these rows that do not contribute any value to our analysis. Also, we see above that our data consists of 10 columns (or variables/features) and 453 rows (or observations). To obtain or display our data dimensions in Python, we use the shape, a data attribute. Python's data attributes are different from functions in that we omit the parentheses "()" after the attribute. Further, above we displayed the counts of missing values in each column of our dataset with isnull (which is similar to isna()) to extract the rows with nas and then tallied them with sum() to find the sum of the rows with the missing values in each column. These methods come in handy when we want to obtain a quick
summary of the rows and columns with missing values in our dataset.

# Delete NAs.
ss_2008_census_df = ss_2008_census_df.dropna(how = 'any', axis = 0)

# Convert column names to the list and print the list.
ss_2008_census_df.columns.to_list()

## ['Region', 'Region Name', 'Region - RegionId', 'Variable', 'Variable Name', 'Age', 'Age Name', 'Scale', 'Units', '2008']

In the preceding code chunk, we first deleted the rows with missing values with dropna() by setting how to “any” and axis to “0” (or rows). Next, we output the column names with column.to_list() to subset our data with the columns of interest. Here, we’re only interested in these columns: ‘Region Name’, ‘Variable Name’, ‘Age Name’, and ‘2008’. And in the below code chunk, we selected these columns using double square brackets ([[]]). Next, we converted the population
column “2008” to integer or numeric values using the astype() method and supplied it with the data type int, short for integer. Lastly, we inspected the first five rows with the head().

# Subset the data by selecting only the columns to focus on.
ss_2008_census_df = ss_2008_census_df[['Region Name', 'Variable Name', 'Age Name', '2008']]

# Convert the population column to integer values.
ss_2008_census_df['2008'] = ss_2008_census_df['2008'].astype('int')

# Inspect the first five rows.
ss_2008_census_df.head()

##   Region Name               Variable Name  Age Name    2008
## 0  Upper Nile  Population, Total (Number)     Total  964353
## 1  Upper Nile  Population, Total (Number)    0 to 4  150872
## 2  Upper Nile  Population, Total (Number)    5 to 9  151467
## 3  Upper Nile  Population, Total (Number)  10 to 14  126140
## 4  Upper Nile  Population, Total (Number)  15 to 19  103804

# Perform feature engineering: split the variable name column by space.
ss_2008_census_df['Gender'] = ss_2008_census_df['Variable Name'].str.split(' ', ).str[1]

# Inspect the first five rows.
ss_2008_census_df.head()

##   Region Name               Variable Name  Age Name    2008 Gender
## 0  Upper Nile  Population, Total (Number)     Total  964353  Total
## 1  Upper Nile  Population, Total (Number)    0 to 4  150872  Total
## 2  Upper Nile  Population, Total (Number)    5 to 9  151467  Total
## 3  Upper Nile  Population, Total (Number)  10 to 14  126140  Total
## 4  Upper Nile  Population, Total (Number)  15 to 19  103804  Total

Using the split()

As you can see above, the “Variable Name” column contains three pieces of information: “Population”, “Total/Male/Female” and “(Number)”, separated by both comma and spaces. This column contains gender information, so we’re only interested in the “Total/Male/Female” portion of this column. Therefore, we split this column into three columns using the split() method (combined with str since this is a string), but we only kept the middle portion using its index.

Selecting And Renaming the Columns To Focus on

# Select desired columns.
ss_2008_census_df_1 = ss_2008_census_df[['Region Name', 'Gender', 'Age Name', '2008']]

# Filter the data to exclude rows with the 'Total.'
ss_2008_census_df_1 = ss_2008_census_df_1[(ss_2008_census_df_1['Gender'] != 'Total') & (ss_2008_census_df_1['Age Name'] != 'Total')]

# Rename the columns: 'Region Name', 'Age Name' and '2008'.
ss_2008_census_df_1 = ss_2008_census_df_1.rename(columns = {'Region Name': 'State', 'Age Name':'Age Category', '2008':'Population'})

In the above chunk, we re-selected the columns we need for our project using the double square brackets, [[]]. Next, we removed the rows with the Total population because we’ll recalculate these values by ourselves. Last, we renamed our columns of interest using the rename() method.

Modifying Column Values with the replace() Method

# Modify state names with replace().
new_age_cats = {'Age Category':{'0 to 4':'0 -9', '5 to 9':'0 -9', '10 to 14':'10 - 19', '15 to 19':'10 - 19', '20 to 24':'20 - 29', '25 to 29':'20 - 29', '30 to 34':'30 - 39', '35 to 39':'30 - 39', '40 to 44':'40 - 49', '45 to 49':'40 - 49', '50 to 54':'50 - 59', '55 to 59':'50 - 59', '60 to 64':'60 +', '65+':'60 +'}}

ss_2008_census_df_1.replace(new_age_cats, inplace = True)

# View the first 5 rows.
ss_2008_census_df_1.head()

##          State Gender Age Category  Population
## 16  Upper Nile   Male         0 -9       82690
## 17  Upper Nile   Male         0 -9       83744
## 18  Upper Nile   Male      10 - 19       71027
## 19  Upper Nile   Male      10 - 19       57387
## 20  Upper Nile   Male      20 - 29       42521

Now that we have the columns we need for our task, let’s create new bins or categories for the Age Category column since there’re too many categories, which would overstretch over data. We do this with the replace() method from the panda
library.
The replace method works as follows:

df.replace(‘old value’, ’new value), for a single value. Or
df.replace([‘old value 1’, ‘old value 2’, ‘old value 3’], [‘new
value 1’, ‘new value 2’, ‘new value 3’]) for a list of values. Or
df.replace({‘colum_name’: {‘old value 1’:‘new value 1’, ‘old value
2’:‘new value 2’}})

The last method is what we used in the above chunk to create new age categories for our dataset.

Computing Aggregates with `groupby()` and `agg()` Methods

# Group the dataset by state, gender, and age category, and then summarize.
ss_2008_census_df_2 = ss_2008_census_df_1.groupby(['State', 'Gender', 'Age Category']).agg(Population = ('Population', 'sum')).reset_index()

# View the first 5 rows.
ss_2008_census_df_2.head()

##                State  Gender Age Category  Population
## 0  Central Equatoria  Female         0 -9      155756
## 1  Central Equatoria  Female      10 - 19      127336
## 2  Central Equatoria  Female      20 - 29      105011
## 3  Central Equatoria  Female      30 - 39       65871
## 4  Central Equatoria  Female      40 - 49       35805

# Group the dataset by state and then summarize.
ss_2008_census_state_totals_df = ss_2008_census_df_2.groupby('State').agg(Population = ('Population', 'sum')).reset_index(level = 'State')

# Rearrange the dataset by population size in descending order.
ss_2008_census_state_totals_df.sort_values(['Population'], ascending = False, inplace = True)

# Reset the index.
ss_2008_census_state_totals_df.reset_index(drop=True)

##                      State  Population
## 0                  Jonglei     1358602
## 1        Central Equatoria     1103557
## 2                   Warrap      972928
## 3               Upper Nile      964353
## 4        Eastern Equatoria      906161
## 5  Northern Bahr el Ghazal      720898
## 6                    Lakes      695730
## 7        Western Equatoria      619029
## 8                    Unity      585801
## 9   Western Bahr el Ghazal      333431

# Group the dataset by state and gender; summarize.
ss_2008_census_state_by_gender_df = ss_2008_census_df_2.groupby(['State', 'Gender']).agg(Population = ('Population', 'sum')).reset_index(level = 'Gender')

# Rearrange the dataset by population size and state in descending order.
ss_2008_census_state_by_gender_df.sort_values(['Population', 'State'], ascending = False, inplace = True)

# Print the dataset.
ss_2008_census_state_by_gender_df

##                          Gender  Population
## State                                      
## Jonglei                    Male      734327
## Jonglei                  Female      624275
## Central Equatoria          Male      581722
## Upper Nile                 Male      525430
## Central Equatoria        Female      521835
## Warrap                   Female      502194
## Warrap                     Male      470734
## Eastern Equatoria          Male      465187
## Eastern Equatoria        Female      440974
## Upper Nile               Female      438923
## Northern Bahr el Ghazal  Female      372608
## Lakes                      Male      365880
## Northern Bahr el Ghazal    Male      348290
## Lakes                    Female      329850
## Western Equatoria          Male      318443
## Western Equatoria        Female      300586
## Unity                      Male      300247
## Unity                    Female      285554
## Western Bahr el Ghazal     Male      177040
## Western Bahr el Ghazal   Female      156391

Now that we have cleaned, manipulated, and transformed our dataset, we will shift our attention to exploratory data analysis (or EDA). So, in the above chunk, we grouped the dataset with the groupby() method using the State column and then computed states’ total populations with the agg() method. Next, we reset the index with the reset_index() method. Additionally, we set ascending to False to not
arrange our dataset in the ascending order and inplace to True`, to overwrite the settings. Further, we sorted the dataset based on the population size and the State column. Last, we displayed the dataset.

Tabulating the Dataset with the `pandas pivot_table()`

# Pivot the dataset.
table = pd.pivot_table(ss_2008_census_df_2, values = 'Population', index = ['State', 'Gender'],
                     aggfunc = np.sum, margins = True, margins_name = 'Total Population')

# Rearrange the dataset by population size and state in descending order.                    
table.sort_values(['Population', 'State'], ascending = False, inplace = True)

# Print the table.
table

##                                 Population
## State                   Gender            
## Total Population                   8260490
## Jonglei                 Male        734327
##                         Female      624275
## Central Equatoria       Male        581722
## Upper Nile              Male        525430
## Central Equatoria       Female      521835
## Warrap                  Female      502194
##                         Male        470734
## Eastern Equatoria       Male        465187
##                         Female      440974
## Upper Nile              Female      438923
## Northern Bahr el Ghazal Female      372608
## Lakes                   Male        365880
## Northern Bahr el Ghazal Male        348290
## Lakes                   Female      329850
## Western Equatoria       Male        318443
##                         Female      300586
## Unity                   Male        300247
##                         Female      285554
## Western Bahr el Ghazal  Male        177040
##                         Female      156391

Here we tabulated our dataset with the pivot_table() method. Next, we sorted the table by Population and State columns, and then we displayed the table.

Marrying `R` with `Python`

Our objective in this article is to demonstrate that data scientists and data analysts (and indeed, data teams) can benefit tremendously by leveraging both R and Python’s powers and strengths instead of viewing these data science programming languages as rivals. Hence, in the below sections, we’ll visualize our dataset with the ggplot2 package, one of the tidyverse core packages.

Visualizing the Data with `ggplot2`

ggplot2 is the most popular R data visualization package by Hadley Wickham. To begin with, we used py$ to call Python’s object within the R’s code chunk in the below chunk. Next, we initialized the canvas with the ggplot() function and assigned the State column to the x-axis and Population column to the y-axis. Additionally, we sorted the State column values using the fct_order() and fct_rev() functions from the forcats package. This orders our dataset in
descending order using the population column.

# Initialize the Canvas.
state_pop_g <- ggplot(py$ss_2008_census_state_totals_df, 
            aes(x = State %>% fct_reorder(Population) %>% fct_rev(), 
                y = Population)) +

       # Add the geometries: we're using seaborn colorblind color #7.
       geom_col(fill = colorblind_palette[[7]]) +

       # Add a theme: tidyquant theme
       tidyquant::theme_tq() +

       # Add the title, x-axis title, and y-axis title.
       labs(
         title    = 'Jonglei State has the largest population',
         x        = '',
         y        = 'Population',
         caption  = 'Caption: South Sudan 2008 Census Data by State in Descending Order.') +

       # Adjust the y-axis scale.
       scale_y_continuous(labels = scales::number_format(scale = 1e-3, suffix = 'K')) +

       # Adjust the y-axis scale.
       scale_x_discrete(labels = py$ss_2008_census_state_totals_df$State %>% str_wrap(width = 15)) +

       # Modify the y-axis limits.
       expand_limits(y = c(0, 1500000)) +

       # Adjust graph attributes.
       theme(
         plot.title          = element_text(hjust  = 0.5, 
                                            margin = margin(0, 0, 15, 0, unit = "pt")),
         plot.subtitle       = element_text(hjust  = 0.5, 
                                            margin = margin(2, 0, 10, 0, unit = "pt")),
         axis.text.x         = element_text(angle  = 45, 
                                            hjust  = 1),
         axis.ticks.x        =  element_blank(),
         panel.grid.major.x  = element_blank()
       ) 

# Display the graph.
state_pop_g

Population by State and Gender

# Subset the data to generate another table using the R data frame.
ss_2008_census_tbl <-  py$ss_2008_census_df_2 %>% 

  # Group by state and gender, and summarize.
  group_by(State, Gender) %>% 
  summarize(Population = sum(Population),
            .groups    = "drop") 

# Display the results in a paged table.
ss_2008_census_tbl

## # A tibble: 20 x 3
##    State                   Gender Population
##    <chr>                   <chr>       <dbl>
##  1 Central Equatoria       Female     521835
##  2 Central Equatoria       Male       581722
##  3 Eastern Equatoria       Female     440974
##  4 Eastern Equatoria       Male       465187
##  5 Jonglei                 Female     624275
##  6 Jonglei                 Male       734327
##  7 Lakes                   Female     329850
##  8 Lakes                   Male       365880
##  9 Northern Bahr el Ghazal Female     372608
## 10 Northern Bahr el Ghazal Male       348290
## 11 Unity                   Female     285554
## 12 Unity                   Male       300247
## 13 Upper Nile              Female     438923
## 14 Upper Nile              Male       525430
## 15 Warrap                  Female     502194
## 16 Warrap                  Male       470734
## 17 Western Bahr el Ghazal  Female     156391
## 18 Western Bahr el Ghazal  Male       177040
## 19 Western Equatoria       Female     300586
## 20 Western Equatoria       Male       318443

# Plot population dataset by state and gender - not ordered.
state_pop_by_gender <- ss_2008_census_tbl %>% 

  # Modify state names to wrap around.
  mutate(State = State %>% str_wrap(width = 8)) %>% 

  # Initialize the canvas.
  ggplot(aes(State, Population)) +

  # Adding geometries.
   geom_bar(aes(fill = Gender), 
            position = "stack",
           stat      = "identity") +

  # Modify the graph with a tidyquant::theme_tq().
   tidyquant::theme_tq() +

   # Add the title, x-axis title, and y-axis title.
   labs(
     title    = 'Jonglei State has the largest population',
     x        = '',
     y        = 'Population',
     caption  = 'Caption: South Sudan 2008 Census Data by State.') +

   # Adjust the y-axis scale.
   scale_y_continuous(labels = scales::number_format(scale = 1e-6, suffix = 'M')) +

   # Adjust y-axis limits.
   expand_limits(y = c(0, 1500000)) +

   # Modify graph attributes.
   theme(
     plot.title          = element_text(hjust  = 0.5, 
                                        margin = margin(0, 0, 15, 0, unit = "pt")),
     plot.subtitle       = element_text(hjust  = 0.5, 
                                        margin = margin(2, 0, 10, 0, unit = "pt")),
     axis.text.x         = element_text( 
                                        hjust  = 1),
     panel.grid.major    = element_blank(),
     panel.grid.minor    = element_blank(),
     legend.position     = "bottom"
   ) 

# Display the graph as an interactive graph using ggplotly().
state_pop_by_gender

# Graph population by state and gender in ascending order.
state_pop_by_gender_g <- ss_2008_census_tbl %>% 

  # Modify state names to wrap around the x-axis.
  mutate(State = State %>% str_wrap(width = 8)) %>% 

  # Instantiate the canvas; order the states by population size.
  ggplot(aes(State %>% fct_reorder(Population), Population)) +

  # Add the geometries.
  geom_bar(aes(fill = Gender), 
          position = "stack", 
          stat     = "identity") +

  # Add a theme: tidyquant::theme_tq().
  tidyquant::theme_tq() +

  # Add the title, x-axis title, and y-axis title.
  labs(
    title    = 'Jonglei State has the largest population',
    x        = '',
    y        = 'Population') +

  # Adjust the y-axis scale.
  scale_y_continuous(labels = scales::number_format(scale = 1e-6, suffix = 'M')) +
  expand_limits(y = c(0, 1500000)) +

  # Adjust the graph attributes.
  theme(
     plot.title          = element_text(hjust  = 0.5,
                                        margin = margin(0, 0, 15, 0, unit = "pt")),
     plot.subtitle       = element_text(hjust  = 0.5,
                                        margin = margin(2, 0, 10, 0, unit = "pt")),
     axis.text.x         = element_text(hjust  = 1),
     panel.grid.major.x  = element_blank(),
     legend.position     = "bottom"
   )

# Display the graph.
state_pop_by_gender_g

In the preceding two sections, we visualized the dataset by state and gender. In the second graph, we plotted the dataset without arranging it (this was intentional!). However, in the third graph, we arranged the dataset by population in ascending order – it is imperative to display the data either in ascending or descending order to avoid confusing your readers.

Conclusion

This article demonstrates how to perform data wrangling with the pandas library and visualize it with ggplot2 in RStudio. Here in this article, we are not saying that this is the actual population of South Sudan, but instead, we used the dataset to highlight a few data wrangling techniques within the pandas library.

On the inferential side, government agencies would draw decisions about public amenities such as schools, kindergartens, maternity wards, and healthcare centers and where to build them from this analysis. The distribution of the national resources and state representations could be determined by this analysis as well. However, that’s not the objective of our post, so we will leave it there.

References

McKinney, W.(2017). Python for Data Analysis, 2nd Edition. O’Reilly
Media, Inc.

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Marrying-R-with-Python-1

Marrying R with Python – 1

Table of Contents

Introduction

Loading `R` Packages

Loading the `pandas` Library and Setting the Printing Options

Transforming the Data

Importing the Dataset

Deleting Rows with Missing Values

Using the split()

Selecting And Renaming the Columns To Focus on

Modifying Column Values with the replace() Method

Computing Aggregates with `groupby()` and `agg()` Methods

Tabulating the Dataset with the `pandas pivot_table()`

Marrying `R` with `Python`

Visualizing the Data with `ggplot2`

Population by State and Gender

Conclusion

References

Data Wrangling with PySpark

Method Chaining in Pandas

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Blog

Marrying R with Python – 1

Table of Contents

Introduction

Loading R Packages

Loading the pandas Library and Setting the Printing Options

Transforming the Data

Importing the Dataset

Deleting Rows with Missing Values

Using the split()

Selecting And Renaming the Columns To Focus on

Modifying Column Values with the replace() Method

Computing Aggregates with groupby() and agg() Methods

Tabulating the Dataset with the pandas pivot_table()

Marrying R with Python

Visualizing the Data with ggplot2

Population by State and Gender

Conclusion

References

You may also like

Jonglei Institute

COMPANY

USEFUL LINKES

JIT NEWSLETTER

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Loading `R` Packages

Loading the `pandas` Library and Setting the Printing Options

Computing Aggregates with `groupby()` and `agg()` Methods

Tabulating the Dataset with the `pandas pivot_table()`

Marrying `R` with `Python`

Visualizing the Data with `ggplot2`