---
title: Reading files in Python
tags: Python Cheatbook
---

# Reading files in Python

Read a single file line by line

import os

your_file = './data/site1/B1/path_data_files/somefile.txt'

if os.path.isfile(your_file):
    f = open(your_file, 'r', encoding='utf-8')
    for line in f:
        print(line) # Do whatever you like here.
    f.close()

Reading files line by line in a directory

import os

your_path = './data/site1/B1/path_data_files'
files = os.listdir(your_path)

print('found {} files'.format(len(files)))

for file in files:
	if os.path.isfile(os.path.join(your_path, file)):
		f = open(os.path.join(your_path, file), 'r', encoding='utf-8')
		for line in f:
			print(line) # Do whatever you like here.
		f.close()

- `os.path.isfile(file)` check if its a valid file.
- `open(path, mode, [encoding])` opens a file with given `mode` and optional `encoding` parameter, usually `encoding='utf-8'`.
- `os.path.join(path, file)` Joins two path.
- `file.close()` Closes the filestream.

# Reading a JSON file

import json

with open(json_file_path) as file:
    json_data = json.load(file)
    print(json_data['myJsonKey'])

- `open(filepath)` opens a file
- `json.load(file)` parses a json filestream to a dictonary object.

# Reading a CSV file [1]

## With `csv` module

import csv

filename = "car.data"
fields = []
rows = []

with open(filename, 'r') as csvfile:
	csvreader = csv.reader(csvfile)
	# uncomment to take first row as fields
	# fields = next(csvreader)
	for row in csvreader:
		rows.append(row)
	print("Total no. of rows: %d" % (csvreader.line_num))
    
print('Field names are:' + ', '.join(field for field in fields))
print('\nFirst 5 rows are:')
print('----------------')

for row in rows[:5]:
	for col in row:
		if col == row[len(row)-1]:
			print(col)
		else:
			print(col, end=',')

- `csv` module makes it easy to read csv
- `csv.reader(csvfile)` prepares a csv reader which can iterated to extract csv data

**Output:**

Total no. of rows: 1728
Field names are:

First 5 rows are:
----------------
vhigh,vhigh,2,2,small,low,unacc
vhigh,vhigh,2,2,small,med,unacc
vhigh,vhigh,2,2,small,high,unacc
vhigh,vhigh,2,2,med,low,unacc
vhigh,vhigh,2,2,med,med,unacc

Alternatively you can use `pandas` library to read the csv into dataframes and do exploratory data anlysis much easily. [Check the `pandas` tutorial to learn more](https://hackmd.io/9kh0_lzMT46HuevPkckrFw#Read-seperated-value-files-CSV-TSV-etc).

# References

1. https://www.geeksforgeeks.org/working-csv-files-python/