Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
Now Reading
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
In Python, reading CSV files is a fundamental task for data processing and analysis. This tutorial offers an introduction and overview of the essential skill of how to read a CSV file in Python.
Reading CSV (Comma-Separated Values) files is fundamental for handling structured data. This process involves importing external data stored in CSV format and converting it into a usable data structure within Python. Python provides efficient methods to accomplish this task by leveraging libraries like pandas.
A CSV file, which stands for "Comma-Separated Values," is a specific file format consisting of tabular data. It serves as a medium for exporting data from various spreadsheet programs like Microsoft Excel, Apple Numbers, or Google Sheets, and is also capable of being imported back into them.
Differing from other spreadsheet file formats, CSVs are characterized by their single-sheet structure. In these files, data elements are primarily separated by commas, although other delimiters like tabs, semicolons, pipes, or carets can be utilized.
CSV files possess universal readability, making them compatible with a wide range of software applications. The delimiter, mostly a comma, acts as the separator between individual values within each row. The first row serves as the header, containing labels or identifiers for each column.
Subsequent rows in a CSV file represent the actual data for each respective column. While some CSV documents lack headers, others contain each data element within double quotation marks.
Pandas uses .csv files because of the following benefits:
Simplicity and readability: CSV files are plain-text files with rows and columns separated by commas, making them human-readable and straightforward to work with programmatically.
Widespread data storage: Countless datasets are shared online or exported from various applications in CSV format. This ubiquity positions CSV as a go-to choice for data import and export tasks.
Data structure compatibility: CSV's tabular format aligns seamlessly with pandas' DataFrames. Each CSV row corresponds to a DataFrame row, and each comma-separated value maps to a DataFrame cell. This inbuilt alignment simplifies the loading of CSV data into pandas.
Ease of sharing: CSV files are portable, platform-agnostic, and universally compatible. Since they are text-based and free from proprietary formats, they can be shared and processed across various data processing tools and platforms.
Lightweight: CSV files are light in terms of both file size and storage requirements. They lack complex formatting or metadata, making them efficient for data storage and transmission.
Flexibility: CSVs support various data types, including numeric, textual, and date/time values. Pandas are adept at automatically detecting and handling these diverse data types when reading CSV files.
Ease of use: Python's pandas' library provides the read_csv() function, a user-friendly tool for effortlessly importing CSV files into DataFrames. This function offers numerous customization options to accommodate various CSV file structures, such as varying delimiters or handling missing data gracefully.
Data transformation and analysis: After importing CSV data into a pandas DataFrame, many dominant utilities come into play, enabling effortless data refinement, exploration, and visualization, including tasks like data cleansing, filtering, aggregation, and visual representation.
Compatibility with various libraries: Pandas harmoniously meshes with vital Python data science and machine learning libraries such as NumPy, Matplotlib, and scikit-learn, fostering a harmonious ecosystem for tasks encompassing data preparation and exploratory analysis in the realm of data science projects.
The csv.reader() function in Python's csv module allows you to read and process CSV files.
import csv
# Open the CSV file for reading
with open('data.csv', 'r') as file:
csv_reader = csv.reader(file)
# Iterate through each row in the CSV file
for row in csv_reader:
print(row)
Explanation:
We open the CSV file named 'data.csv' in read mode using a context manager (with statement). csv.reader() is used to create a reader object for the CSV file. We iterate through the reader object, and each row represents a list of values from a CSV row.
You can use the csv.DictReader() function to read a CSV file into a dictionary.
import csv
# Open the CSV file for reading
with open('data.csv', 'r') as file:
csv_reader = csv.DictReader(file)
# Iterate through each row in the CSV file
for row in csv_reader:
print(row)
Explanation:
csv.DictReader() is used to create a reader object that interprets the first row as column headers and maps each row to a dictionary with column names as keys.
Pandas is a powerful library for working with tabular data, including CSV files. Here's how to read a CSV file using Pandas:
import pandas as pd
# Read the CSV file into a DataFrame
df = pd.read_csv('data.csv')
# Print the first few rows of the DataFrame
print(df.head())
Explanation:
We use pd.read_csv() to read the CSV file into a Pandas DataFrame. df.head() is used to print the first few rows of the DataFrame.
Python's csv module provides functions like csv.reader() and csv.DictReader() for reading CSV files as discussed earlier. It also includes functions for writing CSV files like csv.writer() and csv.DictWriter(). Pandas' read_csv() function is a versatile tool for reading CSV files into DataFrames. You can customize it with various parameters to handle different file formats and configurations.
import pandas as pd
# Sample data as a list of dictionaries
data = [
{"Name": "Alice", "Age": 30, "City": "New York"},
{"Name": "Bob", "Age": 25, "City": "Los Angeles"},
{"Name": "Charlie", "Age": 35, "City": "Chicago"},
]
# Create a DataFrame from the data
df = pd.DataFrame(data)
# Display the DataFrame
print(df)
Explanation:
We import the pandas library as pd. We define a list of dictionaries called data, where each dictionary represents a row of data. Using pd.DataFrame(data), we create a Pandas DataFrame from the data list. Finally, we print the DataFrame, which displays the data in tabular format.
The csv.writer() class in Python's csv module is used to write data to a CSV file. You can write data row by row using this writer.
import csv
# Sample data
data = [
["Name", "Age", "City"],
["Alice", 30, "New York"],
["Bob", 25, "Los Angeles"],
["Charlie", 35, "Chicago"]
]
# Open a CSV file for writing
with open("output.csv", "w", newline="") as file:
csv_writer = csv.writer(file)
# Write data row by row
for row in data:
csv_writer.writerow(row)
print("CSV file 'output.csv' created successfully.")
Result:
The csv.DictWriter() class is used when you have data in the form of dictionaries, where keys represent column headers.
import csv
# Sample data as a list of dictionaries
data = [
{"Name": "Alice", "Age": 30, "City": "New York"},
{"Name": "Bob", "Age": 25, "City": "Los Angeles"},
{"Name": "Charlie", "Age": 35, "City": "Chicago"}
]
# Define the CSV file's column headers
fieldnames = ["Name", "Age", "City"]
# Open a CSV file for writing
with open("output_dict.csv", "w", newline="") as file:
csv_writer = csv.DictWriter(file, fieldnames=fieldnames)
# Write the header row
csv_writer.writeheader()
# Write data row by row
for row in data:
csv_writer.writerow(row)
print("CSV file 'output_dict.csv' created successfully.")
Result:
By default, Pandas prints DataFrames as tables, and you can simply use the print(df) statement to print the DataFrame without using to_string().
import pandas as pd
# Sample data as a list of dictionaries
data = [
{"Name": "Alice", "Age": 30, "City": "New York"},
{"Name": "Bob", "Age": 25, "City": "Los Angeles"},
{"Name": "Charlie", "Age": 35, "City": "Chicago"},
]
# Create a DataFrame from the data
df = pd.DataFrame(data)
# Print the DataFrame without using to_string()
print(df)
You can control the maximum number of rows displayed when printing a DataFrame by setting the pd.options.display.max_rows option. Here's how to check and set it:
import pandas as pd
# Sample data as a list of dictionaries
data = [{"Name": f"Person {i}", "Age": i} for i in range(1, 21)]
# Create a DataFrame from the data
df = pd.DataFrame(data)
# Check the current maximum rows setting
current_max_rows = pd.options.display.max_rows
print(f"Current maximum rows setting: {current_max_rows}")
# Set the maximum rows to display (e.g., 10 rows)
pd.options.display.max_rows = 10
# Print the DataFrame (only 10 rows will be displayed)
print(df)
# Reset the maximum rows to the original setting
pd.options.display.max_rows = current_max_rows
We first check the current maximum rows setting using pd.options.display.max_rows. Then, we set the maximum rows to display to 10 using pd.options.display.max_rows = 10. When we print the DataFrame, only 10 rows are displayed. Finally, we reset the maximum rows to the original setting to avoid affecting future DataFrame printing.
CSV files are the backbone of structured data handling in Python, and using libraries like pandas, Python simplifies the process. Reading CSVs is vital for data work, and whether you're using Spyder or Visual Studio Code, Python's flexibility and simplicity make it a go-to tool for data professionals. By using CSV's readability and pandas' power, Python excels at importing, manipulating, and analyzing tabular data, making it a cornerstone in the world of data science.
1. How to read a CSV file in Python Spyder?
To read a CSV file in Python with Sypder you need to import the Pandas library and then read the file using pd.read_csv(‘data.csv’).
2. How to use Python to extract specific data from a CSV file?
Python and the pandas' library enable data extraction from CSV files through file ingestion into a DataFrame, followed by the implementation of tailored filtering criteria on this DataFrame.
3. How to read a CSV file in Python VS code?
To open a CSV file in Python inside VS Code, start with importing the 'pandas' library, then use 'pd.Read_csv()' to get access to the CSV content, and finally, execute your script to engage with the data.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
Popular
Talk to our experts. We’re available 24/7.
Indian Nationals
1800 210 2020
Foreign Nationals
+918045604032
upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enrolling. upGrad does not make any representations regarding the recognition or equivalence of the credits or credentials awarded, unless otherwise expressly stated. Success depends on individual qualifications, experience, and efforts in seeking employment.
upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...