Let’s face it: you need to get information into and out of your programs through more than just the keyboard and console. Exchanging information through text files is a common way to share info between programs. One of the most popular formats for exchanging data is the CSV format. But how do you use it?
Let’s get one thing clear: you don’t have to (and you won’t) build your own CSV parser from scratch. There are several perfectly acceptable libraries you can use. The Python csv library will work for most cases. If your work requires lots of data or numerical analysis, the pandas library has CSV parsing capabilities as well, which should handle the rest.
In this article, you’ll learn how to read, process, and parse CSV from text files using Python. You’ll see how CSV files work, learn the all-important csv library built into Python, and see how CSV parsing works using the pandas library.
So let’s get started!
What Is a CSV File?
A CSV file (Comma Separated Values file) is a type of plain text file that uses specific structuring to arrange tabular data. Because it’s a plain text file, it can contain only actual text data—in other words, printable ASCII or Unicode characters.
The structure of a CSV file is given away by its name. Normally, CSV files use a comma to separate each specific data value. Here’s what that structure looks like:
column 1 name,column 2 name, column 3 name
first row data 1,first row data 2,first row data 3
second row data 1,second row data 2,second row data 3
...
Notice how each piece of data is separated by a comma. Normally, the first line identifies each piece of data—in other words, the name of a data column. Every subsequent line after that is actual data and is limited only by file size constraints.
In general, the separator character is called a delimiter, and the comma is not the only one used. Other popular delimiters include the tab (\t), colon (:) and semi-colon (;) characters. Properly parsing a CSV file requires us to know which delimiter is being used.
Where Do CSV Files Come From?
CSV files are normally created by programs that handle large amounts of data. They are a convenient way to export data from spreadsheets and databases as well as import or use it in other programs. For example, you might export the results of a data mining program to a CSV file and then import that into a spreadsheet to analyze the data, generate graphs for a presentation, or prepare a report for publication.
CSV files are very easy to work programmatically. Any language that supports text file input and string manipulation (like Python) can work with CSV files directly.
Parsing CSV Files With Python’s Built-in CSV Library
The csv library provides functionality to both read from and write to CSV files. Designed to work out of the box with Excel-generated CSV files, it is easily adapted to work with a variety of CSV formats. The csv library contains objects and other code to read, write, and process data from and to CSV files.
Reading CSV Files With csv
Reading from a CSV file is done using the reader object. The CSV file is opened as a text file with Python’s built-in open() function, which returns a file object. This is then passed to the reader, which does the heavy lifting.
As a reminder, here’s the employee_birthday.txt file:
name,department,birthday month
John Smith,Accounting,November
Erica Meyers,IT,March
Here’s code to read it:
importcsvwithopen('employee_birthday.txt')ascsv_file:csv_reader=csv.reader(csv_file,delimiter=',')line_count=0forrowincsv_reader:ifline_count==0:print(f'Column names are "{", ".join(row)}"')line_count+=1else:print(f'\t{row[0]} works in the {row[1]} department, and was born in {row[2]}.')line_count+=1print(f'Processed {line_count} lines.')This results in the following output:
Column names are "name", "department", "birthday month" John Smith works in the Accounting department, and was born in November. Erica Meyers works in the IT department, and was born in March.Processed 3 lines.
Each row returned by the reader is a list of String elements containing the data found by removing the delimiters. The first row returned contains the column names, which is handled in a special way.
Reading CSV Files Into a Dictionary With csv
Rather than deal with a list of individual String elements, you can read CSV data directly into a dictionary (technically, an Ordered Dictionary) as well.
Again, our input file, employee_birthday.txt is as follows:
name,department,birthday month
John Smith,Accounting,November
Erica Meyers,IT,March
Here’s the code to read it in as a dictionary this time:
importcsvwithopen('employee_birthday.txt',mode='r')ascsv_file:csv_reader=csv.DictReader(csv_file)line_count=0forrowincsv_reader:ifline_count==0:print(f'Column names are "{", ".join(row)}"')line_count+=1print(f'\t{row["name"]} works in the {row["department"]} department, and was born in {row["birthday month"]}.')line_count+=1print(f'Processed {line_count} lines.')This results in the same output as before:
Column names are "name", "department", "birthday month" John Smith works in the Accounting department, and was born in November. Erica Meyers works in the IT department, and was born in March.Processed 3 lines.
Where did the dictionary keys come from? The first line of the CSV file is assumed to contain the keys to use to build the dictionary. If you don’t have these in your CSV file, you should specify your own keys by setting the fieldnames optional parameter to a list containing them.
Optional Python CSV reader Parameters
The reader object can handle different styles of CSV files by specifying additional parameters, some of which are shown below:
delimiter specifies the character used to separate each field. The default is the comma (',').
quotechar specifies the character used to surround fields that contain the delimiter character. The default is a double quote (' " ').
escapechar specifies the character used to escape the delimiter character, in case quotes aren’t used. The default is no escape character.
These parameters deserve some more explanation. Suppose you’re working with the employee_addresses.txt file. Here’s a reminder of how it looks:
name,address,date joined
john smith,1132 Anywhere Lane Hoboken NJ, 07030,Jan 4
erica meyers,1234 Smith Lane Hoboken NJ, 07030,March 2
This CSV file contains three fields: name, address, and date joined, which are delimited by commas. The problem is that the data for the address field also contains a comma to signify the zip code.
There are three different ways to handle this situation:
Use a different delimiter
That way, the comma can safely be used in the data itself. You use the delimiter optional parameter to specify the new delimiter.
Wrap the data in quotes
The special nature of your chosen delimiter is ignored in quoted strings. Therefore, you can specify the character used for quoting with the quotechar optional parameter. As long as that character also doesn’t appear in the data, you’re fine.
Escape the delimiter characters in the data
Escape characters work just as they do in format strings, nullifying the interpretation of the character being escaped (in this case, the delimiter). If an escape character is used, it must be specified using the escapechar optional parameter.
Writing CSV Files With csv
You can also write to a CSV file using a writer object and the .write_row() method:
importcsvwithopen('employee_file.csv',mode='w')asemployee_file:employee_writer=csv.writer(employee_file,delimiter=',',quotechar='"',quoting=csv.QUOTE_MINIMAL)employee_writer.writerow(['John Smith','Accounting','November'])employee_writer.writerow(['Erica Meyers','IT','March'])The quotechar optional parameter tells the writer which character to use to quote fields when writing. Whether quoting is used or not, however, is determined by the quoting optional parameter:
- If
quoting is set to csv.QUOTE_MINIMAL, then .writerow() will quote fields only if they contain the delimiter or the quotechar. This is the default case. - If
quoting is set to csv.QUOTE_ALL, then .writerow() will quote all fields. - If
quoting is set to csv.QUOTE_NONNUMERIC, then .writerow() will quote all fields containing text data and convert all numeric fields to the float data type. - If
quoting is set to csv.QUOTE_NONE, then .writerow() will escape delimiters instead of quoting them. In this case, you also must provide a value for the escapechar optional parameter.
Reading the file back in plain text shows that the file is created as follows:
John Smith,Accounting,November
Erica Meyers,IT,March
Writing CSV File From a Dictionary With csv
Since you can read our data into a dictionary, it’s only fair that you should be able to write it out from a dictionary as well:
importcsvwithopen('employee_file2.csv',mode='w')ascsv_file:fieldnames=['emp_name','dept','birth_month']writer=csv.DictWriter(csv_file,fieldnames=fieldnames)writer.writeheader()writer.writerow({'emp_name':'John Smith','dept':'Accounting','birth_month':'November'})writer.writerow({'emp_name':'Erica Meyers','dept':'IT','birth_month':'March'})Unlike DictReader, the fieldnames parameter is required when writing a dictionary. This makes sense, when you think about it: without a list of fieldnames, the DictWriter can’t know which keys to use to retrieve values from your dictionaries. It also uses the keys in fieldnames to write out the first row as column names.
The code above generates the following output file:
emp_name,dept,birth_month
John Smith,Accounting,November
Erica Meyers,IT,March
Parsing CSV Files With the pandas Library
Of course, the Python CSV library isn’t the only game in town. Reading CSV files is possible in pandas as well. It is highly recommended if you have a lot of data to analyze.
pandas is an open-source Python library that provides high performance data analysis tools and easy to use data structures. pandas is available for all Python installations, but it is a key part of the Anaconda distribution and works extremely well in Jupyter notebooks to share data, code, analysis results, visualizations, and narrative text.
Installing pandas and its dependencies in Anaconda is easily done:
As is using pip/pipenv for other Python installations:
We won’t delve into the specifics of how pandas works or how to use it. For an in-depth treatment on using pandas to read and analyze large data sets, check out Shantnu Tiwari’s superb article on working with large Excel files in pandas.
Reading CSV Files With pandas
To show some of the power of pandas CSV capabilities, I’ve created a slightly more complicated file to read, called hrdata.csv. It contains data on company employees:
Name,Hire Date,Salary,Sick Days remaining
Graham Chapman,03/15/14,50000.00,10
John Cleese,06/01/15,65000.00,8
Eric Idle,05/12/14,45000.00,10
Terry Jones,11/01/13,70000.00,3
Terry Gilliam,08/12/14,48000.00,7
Michael Palin,05/23/13,66000.00,8
Reading the CSV into a pandasDataFrame is quick and straightforward:
importpandasdf=pandas.read_csv('hrdata.csv')print(df)That’s it: three lines of code, and only one of them is doing the actual work. pandas.read_csv() opens, analyzes, and reads the CSV file provided, and stores the data in a DataFrame. Printing the DataFrame results in the following output:
Name Hire Date Salary Sick Days remaining0 Graham Chapman 03/15/14 50000.0 101 John Cleese 06/01/15 65000.0 82 Eric Idle 05/12/14 45000.0 103 Terry Jones 11/01/13 70000.0 34 Terry Gilliam 08/12/14 48000.0 75 Michael Palin 05/23/13 66000.0 8
Here are a few points worth noting:
- First,
pandas recognized that the first line of the CSV contained column names, and used them automatically. I call this Goodness. - However,
pandas is also using zero-based integer indices in the DataFrame. That’s because we didn’t tell it what our index should be. Further, if you look at the data types of our columns , you’ll see pandas has properly converted the Salary and Sick Days remaining columns to numbers, but the Hire Date column is still a String. This is easily confirmed in interactive mode:
>>> print(type(df['Hire Date'][0]))<class 'str'>
Let’s tackle these issues one at a time. To use a different column as the DataFrame index, add the index_col optional parameter:
importpandasdf=pandas.read_csv('hrdata.csv',index_col='Name')print(df)Now the Name field is our DataFrame index:
Hire Date Salary Sick Days remainingName Graham Chapman 03/15/14 50000.0 10John Cleese 06/01/15 65000.0 8Eric Idle 05/12/14 45000.0 10Terry Jones 11/01/13 70000.0 3Terry Gilliam 08/12/14 48000.0 7Michael Palin 05/23/13 66000.0 8
Next, let’s fix the data type of the Hire Date field. You can force pandas to read data as a date with the parse_dates optional parameter, which is defined as a list of column names to treat as dates:
importpandasdf=pandas.read_csv('hrdata.csv',index_col='Name',parse_dates=['Hire Date'])print(df)Notice the difference in the output:
Hire Date Salary Sick Days remainingName Graham Chapman 2014-03-15 50000.0 10John Cleese 2015-06-01 65000.0 8Eric Idle 2014-05-12 45000.0 10Terry Jones 2013-11-01 70000.0 3Terry Gilliam 2014-08-12 48000.0 7Michael Palin 2013-05-23 66000.0 8
The date is now formatted properly, which is easily confirmed in interactive mode:
>>> print(type(df['Hire Date'][0]))<class 'pandas._libs.tslibs.timestamps.Timestamp'>
If your CSV files doesn’t have column names in the first line, you can use the names optional parameter to provide a list of column names. You can also use this if you want to override the column names provided in the first line. In this case, you must also tell pandas.read_csv() to ignore existing column names using the header=0 optional parameter:
importpandasdf=pandas.read_csv('hrdata.csv',index_col='Employee',parse_dates=['Hired'],header=0,names=['Employee','Hired','Salary','Sick Days'])print(df)Notice that, since the column names changed, the columns specified in the index_col and parse_dates optional parameters must also be changed. This now results in the following output:
Hired Salary Sick DaysEmployee Graham Chapman 2014-03-15 50000.0 10John Cleese 2015-06-01 65000.0 8Eric Idle 2014-05-12 45000.0 10Terry Jones 2013-11-01 70000.0 3Terry Gilliam 2014-08-12 48000.0 7Michael Palin 2013-05-23 66000.0 8
Writing CSV Files With pandas
Of course, if you can’t get your data out of pandas again, it doesn’t do you much good. Writing a DataFrame to a CSV file is just as easy as reading one in. Let’s write the data with the new column names to a new CSV file:
importpandasdf=pandas.read_csv('hrdata.csv',index_col='Employee',parse_dates=['Hired'],header=0,names=['Employee','Hired','Salary','Sick Days'])df.to_csv('hrdata_modified.csv')The only difference between this code and the reading code above is that the print(df) call was replaced with df.to_csv(), providing the file name. The new CSV file looks like this:
Employee,Hired,Salary,Sick DaysGraham Chapman,2014-03-15,50000.0,10John Cleese,2015-06-01,65000.0,8Eric Idle,2014-05-12,45000.0,10Terry Jones,2013-11-01,70000.0,3Terry Gilliam,2014-08-12,48000.0,7Michael Palin,2013-05-23,66000.0,8
Conclusion
If you understand the basics of reading CSV files, then you won’t ever be caught flat footed when you need to deal with importing data. Most CSV reading, processing, and writing tasks can be easily handled by the basic csv Python library. If you have a lot of data to read and process, the pandas library provides quick and easy CSV handling capabilities as well.
Are there other ways to parse text files? Of course! Libraries like ANTLR, PLY, and PlyPlus can all handle heavy-duty parsing, and if simple String manipulation won’t work, there are always regular expressions.
But those are topics for other articles…
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