By: Clinton Brownley

Re-posted from: https://cbrownley.wordpress.com/2015/07/26/intro-to-julia-filtering-rows-with-r-python-and-julia/

In one of my earlier posts I introduced the Julia programming language by comparing how you can read and write CSV files in R, Python, and Julia. In this post I’d like to build on that comparison by describing how you can filter for specific rows in a data set in each language based on a filtering condition, set of interest, and pattern (i.e. regular expression). We’ll be using the same wine quality data set we used in the earlier post, which is available here: wine quality

We’ll use the same three row filters in all of the examples so it’s easy to confirm that the output is consistent across all three languages:

The first row filter specifies a condition that restricts the output to rows where the value in the quality column is greater than 7. The second row filter specifies a set of interest that restricts the output to rows where the value in the quality column is either 7 or 8. The third row filter specifies a pattern that restricts the output to rows where the value in the quality column contains an 8.

R

To begin, let’s see how you can filter for specific rows in R. First we’ll cover how to filter for rows with base R, and then we’ll describe how to accomplish the same task with the data.table and dplyr packages, which are popular packages for managing data in R.

Base R

The following script illustrates how to read data into a data frame, filter for specific rows based on a filter condition, set of interest, and pattern, and write the output of interest to an output file. All three filtering sections show two different ways to filter the rows, first using row indexing and then using the subset function.

Copy and paste the following code into a text file and then save the file as filter_rows.R

#!/usr/bin/env Rscript

# For more information, visit: https://cbrownley.wordpress.com/

#Collect the command line arguments into a variable called args

args <- commandArgs(trailingOnly = TRUE)

# Assign the first command line argument to a variable called input_file

input_file <- args[1]

# Assign the second command line argument to a variable called output_file

output_file <- args[2]

# Use R’s read.csv function to read the data into a variable called wine

# read.csv expects a CSV file with a header row, so

# sep = ',' and header = TRUE are default values

# stringsAsFactors = FALSE means don’t convert character vectors into factors

wine <- read.csv(input_file, sep = ',', header = TRUE, stringsAsFactors = FALSE)

# Row Value Meets Specific Condition

wine_condition <- wine[which(wine$quality > 7), ]

# Using subset function

wine_condition <- subset(wine, quality > 7)

#print(wine_condition)

# Row Value In Set of Interest

set_of_interest <- c(7, 8)

wine_set <- wine[which(wine$quality %in% set_of_interest), ]

# Using subset function

wine_set <- subset(wine, quality %in% set_of_interest)

#print(wine_set)

# Row Value Matches Specific Pattern

pattern <- '^8$'

wine_pattern <- wine[grep(pattern, wine$quality, ignore.case=TRUE, perl=TRUE), ]

# Using subset function

#wine_pattern <- subset(wine, quality==pattern)

print(wine_pattern)

# Use R’s write.csv function to write the data in the variable wine to the output file

write.csv(wine_pattern, file = output_file, row.names = FALSE)

The section that filters for rows based on a condition only includes one condition, i.e. wine$quality > 7, but you can add more conditions with ANDs, &, and ORs, |. For example, to filter for rows where quality > 7 AND alcohol > 13.0 you can use:

wine_condition <- wine[which(wine$quality > 7 & wine$alcohol > 13.0), ]

Similarly, to filter for rows where quality < 4 OR alcohol > 13.0 you can use:

wine_condition <- wine[which(wine$quality < 4 | wine$alcohol > 13.0), ]

If instead you want to exclude a selection of rows you can negate the which function with a dash, -, like this:

wine_condition <- wine[-which(wine$quality > 7), ]

The section that filters for rows based on a set of interest uses the which function and the %in% binary operator to select rows where the value in the quality column is one of the two values in the set of interest. It’s convenient to assign the values of interest to a variable and then use the variable in the filtering condition so that if the values of interest change you only have to make one change where the values are assigned to the variable.

The section that filters for rows based on a pattern uses the Unix-inspired grep command to select rows where the pattern appears somewhere in the value in the quality column. The ^ metacharacter indicates that the 8 appears at the beginning of the value and the $ metacharacter indicates that the 8 appears at the end of the value, so enclosing the 8 between both ensures that grep looks for rows where 8 is the only value in the quality column. The ignore.case argument isn’t necessary in this case since we’re looking for number, but I included it to show you that it’s available and where to put it if you need it. Similarly, the ^ and $ metacharacters and perl argument aren’t necessary either since we’re searching for a simple number, but I included them to demonstrate how you can use a regular expression and the perl argument to search for a specific pattern.

Now run the following two commands in a Terminal window to make the script executable and to run the script:

chmod +x filter_rows.R

./filter_rows.R winequality-red.csv output/output_R.csv

When you run these commands you’ll see the following output printed to your Terminal screen. In addition, the set of rows matching the pattern in the final filtering section have been written to a CSV file in the output folder inside your current folder.

R package: data.table

Now that we know how to filter for specific rows in base R, let’s discuss how to filter for rows with the data.table package. The following script illustrates how to do so. Copy and paste the following code into a text file and then save the file as filter_rows_data_table.R

#!/usr/bin/env Rscript

require(data.table)

args <- commandArgs(trailingOnly = TRUE)

input_file <- args[1]

output_file <- args[2]

wine <- fread(input_file)

# Row Value Meets Specific Condition

wine_condition <- wine[quality > 7]

# Row Value In Set of Interest

set_of_interest <- c(7, 8)

wine_set <- wine[quality %in% set_of_interest]

# Row Value Matches Specific Pattern

pattern <- '8'

wine_pattern <- wine[quality==pattern]

print(wine_pattern)

write.csv(wine_pattern, file = output_file, row.names = FALSE)

The filtering syntax is very similar to the syntax we used in the base R versions, so you can look in the base R section above for some explanations about the syntax. Now make the script executable and then run the script:

chmod +x filter_rows_data_table.R

./filter_rows_data_table.R winequality-red.csv output/output_R_data_table.csv

When you run these commands you’ll see the same output as you saw with base R printed to your Terminal screen and you’ll have written another CSV file in the output folder.

R package: dplyr

Now let’s see how to filter for rows with the dplyr package. Copy and paste the following code into a text file and then save the file as filter_rows_dplyr.R

#!/usr/bin/env Rscript

require(data.table)

require(dplyr)

args <- commandArgs(trailingOnly = TRUE)

input_file <- args[1]

output_file <- args[2]

wine <- fread(input_file)

wine <- tbl_df(wine)

# Row Value Meets Specific Condition

wine_condition <- wine %>% filter(quality > 7)

# Row Value In Set of Interest

set_of_interest <- c(7, 8)

wine_set <- wine %>% filter(quality %in% set_of_interest)

# Row Value Matches Specific Pattern

pattern <- '8'

wine_pattern <- wine %>% filter(quality==pattern)

print(wine_pattern)

write.csv(wine_pattern, file = output_file, row.names = FALSE)

All three filtering sections use the %>% operator, pulled into dplyr from the magrittr package, and dplyr’s own filter function. The %>% operator is pronounced, “then”, as in “do this, then, do that”. It takes the result of the operation on the left-hand-side of the operator and passes it as the first argument to the operation on the right-hand-side of the operator.

In all three filtering sections we’re simply using it to pass the data set into the filter function. Chaining these two operations doesn’t gain us much – it’s simply to demonstrate how you can use the %>% operator to chain operations together to make your code easier to read and understand. As you’d guess, the filter function filters for rows in the data set with a value that meets the filtering criterion or criteria. Now make the script executable and then run the script:

chmod +x filter_rows_dplyr.R

./filter_rows_dplyr.R winequality-red.csv output/output_R_dplyr.csv

As before, when you run these commands you’ll see the same output as you saw with base R and the data.table package printed to your Terminal screen and you will have written another CSV file in the output folder.

PYTHON

Now that we know how to filter for specific rows in R, let’s discuss how to filter for rows in Python. First we’ll cover how to filter for rows with base Python, and then we’ll describe how to accomplish the same tasks with Pandas, which is a popular package for managing data in Python.

Base Python

The following script illustrates how to process a CSV file line by line, filter for specific rows based on a filter condition, set of interest, and pattern, and write the results to an output file.

Copy and paste the following code into a text file and then save the file as filter_rows.py

#!/usr/bin/env python

# For more information, visit: https://cbrownley.wordpress.com/

# Import Python's built-in csv and sys modules, which have functions

# for processing CSV files and command line arguments, respectively

import csv

import re

import sys

# Assign the first command line argument to a variable called input_file

input_file = sys.argv[1]

# Assign the second command line argument to a variable called output_file

output_file = sys.argv[2]

header_row = True

# Open the input file for reading and close automatically at end

with open(input_file, 'rU') as csv_in_file:

# Open the output file for writing and close automatically at end

with open(output_file, 'wb') as csv_out_file:

# Create a file reader object for reading all of the input data

filereader = csv.reader(csv_in_file)

# Create a file writer object for writing to the output file

filewriter = csv.writer(csv_out_file)

# Use a for loop to process the rows in the input file one-by-one

for row in filereader:

# Process the header row separately from the data rows

# Print it to the screen, write it to the output file, and then

# indicate that you're finished with the header row

if header_row == True:

print row

filewriter.writerow(row)

header_row = False

# Process the data rows according to three filtering conditions

else:

# Row Value Meets Specific Condition

#if int(row[11]) > 7:

#print row

#filewriter.writerow(row)

# Row Value In Set of Interest

#set_of_interest = [7, 8]

#if int(row[11]) in set_of_interest:

#print row

#filewriter.writerow(row)

# Row Value Matches Specific Pattern

pattern = re.compile(r'(?P<my_pattern>8)', re.I)

result = pattern.search(row[11])

if result == None:

pass

else:

print row

filewriter.writerow(row)

We process the header row separately from the data rows because we don’t want to test the header row against the filtering conditions. The built-in csv module reads each row from the input file as a list, a.k.a. array, so we use list indexing, row[11], to access the values in the quality column, which is the twelfth column in the data set (in Python, the first array index is 0). The first two filtering sections are fairly straightforward, i.e. in the first section, the integer version of the value in the quality column is > 7 and in the second section it is one of the values in the set of interest. These two sections are currently commented out with # symbols, but you can uncomment the sections one-at-a-time to see how the output changes.

The third section uses the re module to create a regular expression, search for the pattern in the quality column, and print and write the row when the value in the quality column matches the pattern. The re.I argument makes the pattern case-insensitive. As we said in the R section, we don’t need the argument in this case but it’s helpful to know where to include it if you need it. Now make the script executable and then run the script:

chmod +x filter_rows.py

./filter_rows.py winequality-red.csv output/output_Python.csv

When you run these commands you’ll see the following output printed to your Terminal screen. In addition, the header row and the set of rows matching the pattern in the final filtering section have been written to a CSV file in the output folder.

Python package: Pandas

Now let’s see how to filter for rows with Pandas. Copy and paste the following code into a text file and then save the file as filter_rows_pandas.py

#!/usr/bin/env python

import sys

import string

import pandas as pd

input_file = sys.argv[1]

output_file = sys.argv[2]

data_frame = pd.read_csv(input_file)

# Row Value Meets Specific Condition

data_frame_value_meets_condition = data_frame[data_frame['quality'].astype(int) > 7]

# Row Value In Set of Interest

set_of_interest = [7, 8]

data_frame_value_in_set = data_frame[data_frame['quality'].isin(set_of_interest)]

# Row Value Matches Specific Pattern

data_frame_value_matches_pattern = data_frame[data_frame['quality'].astype(str).str.contains("8")]

print(data_frame_value_matches_pattern)

data_frame_value_matches_pattern.to_csv(output_file, index=False)

In the first filtering section we select the quality column, convert the values into integers, and then test whether they’re greater than 7. In the second section we use the isin function to test whether the value in the quality column is one of the values in the set of interest. Finally, in the third section, we use the contains function to test whether the value in the quality column contains 8. There are also startswith and endswith functions in case you need to test whether the value starts with or ends with a specific pattern.

Pandas also has a convenient .ix function that you can use to filter for specific rows and columns at the same time. Here’s how you could modify the first filtering section to use the .ix function: data_frame_value_meets_condition = data_frame.ix[data_frame.quality.astype(int) > 7, :]

You can select the column by typing data_frame.column. Like R, you need to separate the rows and columns sections with a comma, and you use a colon to indicate that you want to select all of the rows or columns (In this case we want to select all of the columns). Now run the following two commands to make the script executable and to run the script:

chmod +x filter_rows_pandas.py

./filter_rows_pandas.py winequality-red.csv output/output_Python_Pandas.csv

When you run these commands you’ll see similar output as you saw with base Python printed to your Terminal screen, although it will be formatted differently. In addition, the header row and the set of rows matching the pattern in the final filtering section have been written to a CSV file in the output folder.

JULIA

Now that we know how to filter for specific rows in Python, let’s discuss how to filter for rows in Julia. First we’ll cover how to filter for rows with base Julia, and then we’ll describe how to accomplish the same tasks with DataFrames, which is a popular package for managing data in Julia.

Base Julia

The following script illustrates how to read a CSV file line by line, filter for specific rows based on a filter condition, set of interest, and pattern, and write the output of interest to an output file.

Copy and paste this code into a text file and then save the file as filter_rows.jl

#!/usr/bin/env julia

# For more information, visit: https://cbrownley.wordpress.com/

# Assign the first command line argument to a variable called input_file

input_file = ARGS[1]

# Assign the second command line argument to a variable called output_file

output_file = ARGS[2]

# Open the output file for writing

out_file = open(output_file, "w")

header_row = true

# Open the input file for reading and close automatically at end

open(input_file, "r") do in_file

# Use a for loop to process the rows in the input file one-by-one

for row in eachline(in_file)

if header_row == true

print(row)

write(out_file, row)

global header_row = false

else

row_array = map(float, split(strip(row), ","))

#println(row_array)

# Row Value Meets Specific Condition

if row_array[12] > 7.0

#print(join(row_array, ",") * "

")

#write(out_file, join(row_array, ",") * "

")

end

# Row Value In Set of Interest

set_of_interest = Set(7.0, 8.0)

if in(row_array[12], set_of_interest)

#print(join(row_array, ",") * "

")

#write(out_file, join(row_array, ",") * "

")

end

# Row Value Matches Specific Pattern

pattern = r"8$"

if ismatch(pattern, row)

print(row)

write(out_file, row)

end

# Close the if-else statement

end

# Close the for loop

end

# Close the input file handle

end

# Close the output file handle

#close(out_file)

Let’s explain some of the syntax in this script that’s different from R and Python. For example, the open(…) do statement creates an anonymous function with its own scope, so when we initially define the variable header_row above the open(…) do statement and then assign a new value to the variable inside the open(…) do statement we have to precede the variable name with the keyword global.

It’s helpful to keep in mind that for, while, try, and let blocks also default to local scopes, but they do inherit from a parent scope like the one created by the open(…) do statement. Therefore, if we initially define the variable header_row right beneath the open(…) do statement, then the for loop will inherit the variable from the parent scope and we won’t need to precede the variable name with the keyword global. That is, the following alternative syntax would work too:

open(input_file, "r") do in_file

header_row = true

# Use a for loop to process the rows in the input file one-by-one

for row in eachline(in_file)

if header_row == true

print(row)

write(out_file, row)

header_row = false

Like base Python without the csv module, Julia reads each row from the file in as a string, so we use the strip function to remove the trailing newline character, then the split function to split the string on commas and convert it into an array, and finally we map the float function to each of the elements in the array to convert all of the values to floating-point numbers.

The println function adds a newline character on the end of the line before printing the line to the screen whereas the print function does not, it prints the line as-is.

The first two row filtering sections use the join function and the string concatenation symbol, *, to create the row of output that will be printed to the screen and written to the output file. In this case, the join function places commas between each of the elements in the array and converts it to a string. Then we add a newline character to the end of the string with the * concatenation symbol.

We test whether the value in the quality column (in Julia, the first array index is 1), is in the set of interest with the in function. Similarly, we test whether the pattern appears in the row using the ismatch function. You’ll notice that we leave the row as a string, i.e. we don’t convert it into an array, to use the ismatch function since ismatch looks for the pattern in a string, not an array.

Now run the following two commands to make the script executable and to run the script:

chmod +x filter_rows.jl

./filter_rows.jl winequality-red.csv output/output_Julia.csv

When you run these commands you’ll see the following printed to your Terminal screen. In addition, the header row and the set of rows matching the pattern in the final filtering section have been written to a CSV file in the output folder.

Julia packages: DataFrames and DataFramesMeta

Now that we know how to filter for specific rows in base Julia, let’s discuss how to filter for rows with DataFrames, a popular package for managing tabular data in Julia. The following script illustrates how to do so. Copy and paste this code into a text file and then save the file as filter_rows_data_frames.jl

#!/usr/bin/env julia

using DataFrames

using DataFramesMeta

input_file = ARGS[1]

output_file = ARGS[2]

data_frame = readtable(input_file, separator = ',')

# Row Value Meets Specific Condition

data_frame_value_meets_condition = data_frame[data_frame[:quality] .> 7, :]

#data_frame_value_meets_condition = data_frame[(data_frame[:quality] .== 7) | (data_frame[:quality] .== 8), :]

#println(data_frame_value_meets_condition)

# Row Value In Set of Interest

set_of_interest = Set(7, 8)

data_frame_value_in_set = data_frame[findin(data_frame[:quality], set_of_interest), :]

#println(data_frame_value_in_set)

# Row Value Matches Specific Pattern

pattern = r"8"

data_frame_value_matches_pattern = data_frame[[ismatch(pattern, string(value)) for value in data_frame[:quality]], :]

#data_frame_value_matches_pattern = @where(data_frame, [ismatch(pattern, string(value)) for value in :quality])

println(data_frame_value_matches_pattern)

writetable(output_file, data_frame_value_matches_pattern)

The first filtering section demonstrates how you can use one condition or multiple conditions to filter for specific rows. In Julia, you precede the comparison operator with a period, for example .==, to do element-wise comparisons. To use multiple conditions you wrap each one in parentheses and combine them with ANDs, &, or ORs, |. The colon has the same meaning that it does in R and Python. In this case, we’re using it to retain all of the columns.

We use the findin function to determine which rows have the value 7 or 8 in the quality column in order to retain these rows.

The third filtering section demonstrates two slightly different ways to filter for rows based on a pattern. Both methods use row indexing, the ismatch function, and array comprehensions to look for the pattern in each of the elements in the quality column/array. The only real difference between the two methods is that in the first method we have to specify data_frame[…] twice, whereas the second method uses the @where meta-command from the DataFramesMeta package to enable us to refer to the data_frame once and then refer to the quality column with :quality instead of the slightly more cumbersome data_frame[quality].

Now run the following two commands to make the script executable and to run the script:

chmod +x filter_rows_data_frames.jl

./filter_rows_data_frames.jl winequality-red.csv output/output_Julia_DataFrames.csv

When you run these commands you’ll see similar output as you saw with base Julia printed to your Terminal screen, although it will be formatted differently. In addition, the header row and the set of rows matching the pattern in the final filtering section have been written to a CSV file in the output folder.

As you can see, when it comes to filtering for specific rows, the differences in syntax between Python and Julia are very slight. For example, Python’s “if value in set_of_interest” statements are “in(value, set_of_interest)” statements in Julia, and Python’s pattern matching “pattern.search()” statements are “ismatch()” statements in Julia. On the other hand, one difference to keep in mind is that for loops in Julia default to local scope so if you’ve defined a variable outside of a for loop and you need to use it inside the for loop, then you need to precede the variable name with the word global.

Now that we know how to read and write data in a CSV-formatted input file and filter for specific rows with R, Python, and Julia, the next step is to figure out how to filter for specific columns in these languages. Then we can move on to processing lots of files in a directory and also dealing with Excel files. We’ll cover these topics in future posts.

I’d like to thank the Julia users group, especially Nils Gudat and David Gold, for helping me figure out how to use the findin and ismatch functions to filter for specific rows while using the DataFrames package.

Filed under: Analytics, General, Julia, Management, Python, R Tagged: CSV, data.table, DataFrames, DataFramesMeta, dplyr, Filter Rows, Julia, Pandas, Python, R, Wine Quality

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