[Tut] How to Read Specific Columns from CSV File in Python

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How to Read Specific Columns from CSV File in Python

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Table of Contents

• Method 1: Using Pandas
  • ➤ List-Based Indexing of a DataFrame
    
  
  
• Method 2: Integer Based Indexing with iloc
  
  
• Method 3: Name-Based Indexing with loc()
  
  
• Method 4: Using csv Module
  
  
• Conclusion
  
  
• Learn Pandas the Fun Way by Solving Code Puzzles
  

A Quick Glance at The Solutions [Each solution stays for 5-10 secs.]

Problem: Given a CSV file, how to read only specific column(s) from the csv file? (Reading a specific column from a csv file will yield all the row values pertaining to that column.)

Example: Consier the following

Code:

csv

file (

Code:

countries.csv

):

Code:

Country,Capital,Population,Area Germany,Berlin,"84,267,549","348,560" France,Paris,"65,534,239","547,557" Spain,Madrid,"46,787,468","498,800" Italy,Rome,"60,301,346","294,140" India,Delhi,"1,404,495,187","2,973,190" USA,Washington,"334,506,463","9,147,420" China,Beijing,"1,449,357,022","9,388,211" Poland,Warsaw,"37,771,789","306,230" Russia,Moscow,"146,047,418","16,376,870" England,London,"68,529,747","241,930"

Question: How will you read the above csv file and display the following columns

1. Code:

   Country
   column along with the
   Code:

   Capital
   column?
   
   
2. All values in the
   Code:

   population
   column?
   

[b]Method 1: Using Pandas[/b]

Using the Pandas library is probably the best option if you are dealing with csv files. You can easily read a csv file and store an entire column within a variable.

Code:

Code:

import pandas as pd df = pd.read_csv("countries.csv") country = df['Country'] # or # country = df.Country capital = df['Capital'] # or # capital = df.Capital # displaying selected columns (Country and Capital) for x, y in zip(country, capital): print(f"{x} {y}") # displaying a single column (Country) print() print(df['Population'])

Output:

Code:

Germany Berlin France Paris Spain Madrid Italy Rome India Delhi USA Washington China Beijing Poland Warsaw Russia Moscow England London 0 84,267,549 1 65,534,239 2 46,787,468 3 60,301,346 4 1,404,495,187 5 334,506,463 6 1,449,357,022 7 37,771,789 8 146,047,418 9 68,529,747 Name: Population, dtype: object

Explanation:

• Read the csv file using
  Code:

  pd.read_csv()
  Pandas function.
  
  
• Save all the information of the columns Country and Capital within independent variables using
  • Code:

    country = df['Country']
    • Alternatively, you can also use
      Code:

      country = df.Country
    
    
  • Code:

    capital = df['Capital']
    • Alternatively, you can also use
      Code:

      capital = df.Capital

• To display the country names and their capitals simultaneously, you can bind the two columns,
  Code:

  country
  and
  Code:

  capital
  , using the zip() function and then display each country along with its capital using a for loop upon the zipped object.
  
  
• To display all the values in the population column, you can simply use
  Code:

  df['Population']
  .
  

TRIVIA

Code:

zip()

 is a built-in function in Python that takes an arbitrary number of iterables and binds them into a single iterable, a

Code:

zip

object. It combines the 

Code:

n-th

 value of each iterable argument into a tuple.
Read more about zip() here.

[b]➤ List-Based Indexing of a DataFrame[/b]

In case you are not comfortable with using

Code:

zip()

to display multiple columns at once, you have another option. You can simply use list-based indexing to accomplish your goal.

List-based indexing is a technique that allows you to pass multiple column names as a list within the square-bracket selector.

Example:

Code:

import pandas as pd df = pd.read_csv("countries.csv") print() print(df[['Country', 'Capital']])

Output:

Code:

Country Capital 0 Germany Berlin 1 France Paris 2 Spain Madrid 3 Italy Rome 4 India Delhi 5 USA Washington 6 China Beijing 7 Poland Warsaw 8 Russia Moscow 9 England London

[b]Method 2: Integer Based Indexing with iloc[/b]

Approach: The idea here is to use the 

Code:

df.iloc[rows, columns].values

 to access individual columns from the DataFrame using indexing. Note that the first column always has the index 0, while the second column has index 1, and so on.

• Code:

  rows
  is used to select individual rows. Use the slicing colon
  Code:

  :
   to ensure all rows have been selected.
  
  
• Code:

  columns
  is used to select individual columns.
  • Use
    Code:

    country = data.iloc[:, 0].values
    to save the values of the Country column.
    
    
  • Code:

    capital = data.iloc[:, 1].values
    to save the values of the Capital column.
    
    
  • Code:

    population = data.iloc[:, 2].values
    to save the values of the Population column.
    

Code:

import pandas as pd data = pd.read_csv('countries.csv') country = data.iloc[:, 0].values capital = data.iloc[:, 1].values population = data.iloc[:, 2].values # displaying selected columns print(data[['Country', 'Capital']]) print() # displaying a single column (Population) print(population)

Output:

Code:

Country Capital 0 Germany Berlin 1 France Paris 2 Spain Madrid 3 Italy Rome 4 India Delhi 5 USA Washington 6 China Beijing 7 Poland Warsaw 8 Russia Moscow 9 England London ['84,267,549' '65,534,239' '46,787,468' '60,301,346' '1,404,495,187' '334,506,463' '1,449,357,022' '37,771,789' '146,047,418' '68,529,747']

[b]Method 3: Name-Based Indexing with loc()[/b]

Instead of selecting the columns by their index, you can also select them by their name using the

Code:

df.loc[]

selecter.

The following example shows how to select the columns

Code:

Country

and

Code:

Capital

from the given DataFrame.

Code:

import pandas as pd data = pd.read_csv('countries.csv') val = data.loc[:, ['Country', 'Capital']] print(val)

Output:

Code:

Country Capital 0 Germany Berlin 1 France Paris 2 Spain Madrid 3 Italy Rome 4 India Delhi 5 USA Washington 6 China Beijing 7 Poland Warsaw 8 Russia Moscow 9 England London

Related Tutorial: Slicing Data from a Pandas DataFrame using .loc and .iloc

[b]Method 4: Using csv Module[/b]

Code:

csv

module is yet another spectacular option in Python that allows you to play with csv files. Let us have a look at the code that helps us to read the given csv file and then read specific columns from it:

Code:

import csv population = [] with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile: data = csv.DictReader(csvfile) for r in data: print("Country", ":", "Capital") # append values from population column to population list population.append(r['Population']) # displaying specific columns (Country and Capital) print(r['Country'], ":", r['Capital']) # display the population list print(population)

Output:

Code:

Country : Capital Germany : Berlin Country : Capital France : Paris Country : Capital Spain : Madrid Country : Capital Italy : Rome Country : Capital India : Delhi Country : Capital USA : Washington Country : Capital China : Beijing Country : Capital Poland : Warsaw Country : Capital Russia : Moscow Country : Capital England : London ['84,267,549', '65,534,239', '46,787,468', '60,301,346', '1,404,495,187', '334,506,463', '1,449,357,022', '37,771,789', '146,047,418', '68,529,747']

Explanation:

• Import the
  Code:

  csv
  module and open up the csv file. Ensure that you feed in the
  Code:

  encoding
  argument as it helps to eliminate any unreadable characters that may occur in the given csv file.
  • Code:

    with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile
  
  
• Allow Python to read the csv file as a dictionary using
  Code:

  csv.Dictreader
  object.
  
  
• Once the file has been read in the form of a dictionary, you can easily fetch the values from respective columns by using the keys within square bracket notation from the dictionary. Here each column represents the key within the given dictionary.
  

Bonus: Here’s a quick look at how the

Code:

DictReader()

class looks like:

Code:

import csv population = [] with open('countries.csv', newline='', encoding='utf-8-sig') as csvfile: data = csv.DictReader(csvfile) for row in data: print(row)

Output:

Code:

{'Country': 'Germany', 'Capital': 'Berlin', 'Population': '84,267,549', 'Area': '348,560'} {'Country': 'France', 'Capital': 'Paris', 'Population': '65,534,239', 'Area': '547,557'} {'Country': 'Spain', 'Capital': 'Madrid', 'Population': '46,787,468', 'Area': '498,800'} {'Country': 'Italy', 'Capital': 'Rome', 'Population': '60,301,346', 'Area': '294,140'} {'Country': 'India', 'Capital': 'Delhi', 'Population': '1,404,495,187', 'Area': '2,973,190'} {'Country': 'USA', 'Capital': 'Washington', 'Population': '334,506,463', 'Area': '9,147,420'} {'Country': 'China', 'Capital': 'Beijing', 'Population': '1,449,357,022', 'Area': '9,388,211'} {'Country': 'Poland', 'Capital': 'Warsaw', 'Population': '37,771,789', 'Area': '306,230'} {'Country': 'Russia', 'Capital': 'Moscow', 'Population': '146,047,418', 'Area': '16,376,870'} {'Country': 'England', 'Capital': 'London', 'Population': '68,529,747', 'Area': '241,930'}

It is evident from the output that

Code:

csv.DictReader()

returns a dictionary for each row such that the column header is the key while the value in the row is the associated value in the dictionary.

[b]Conclusion[/b]

To sum things up, there are majorly four different ways of accessing specific columns from a given csv file:

• List-Based Indexing.
  
  
• Integer-Based Indexing.
  
  
• Name-Based Indexing.
  
  
• Using csv modules
  Code:

  DictReader
  class.
  

Feel free to use the one that suits you best. I hope this tutorial helped you. Please subscribe and stay tuned for more interesting tutorials. Happy learning!

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