In this Python NumPy tutorial, we will discuss **Python NumPy read CSV** and also we will cover the below examples:

- Python NumPy read CSV with header
- Python NumPy read CSV file
- Python NumPy read CSV Shape
- Python NumPy read CSV into 2d NumPy array
- Python NumPy read CSV pandas
- Python NumPy read CSV skip_rows
- Python NumPy read CSV Data type
- Python NumPy read CSV Size

## Python NumPy read CSV

- CSV basically stands for common separated values. It is used for storing tabular data in a spreadsheet or database.
- Now here each line of the file is called a record and each record consists of files separated by commas which are also known as delimiters.
- Each of the records of the text file is also a part of this file.
- Data is basically into a form of unstructured form it is organizing this large amount of data better.
- One of the uses that would come in handy is the CSV format. Since CSV files are of plain text format it basically makes it very easy and nifty for the website developers.
- The CSV operation basically consists of reading a CSV, writing to a CSV file.
- The CSV file is always opened as a text file format with Python’s built-in function, which returns a file object.

**Example:**

Let’s take an example to check how to read a csv file in Python

```
#read input file
from numpy import loadtxt
file = open('/home/arvind/Documents/test.csv', 'rb')
data = loadtxt(file,delimiter = ",")
print(data)
```

- In the above code, we have opened the output.csv file in reading mode using the open() method.
- Then the file. read() function is used to read the file which returns an iterable sequence read object.
- In the given example you have to provide your own CSV file path.

Here is the Screenshot of the following given code

Read: Python NumPy Random

## Python NumPy read CSV with header

- In this section, we will learn about NumPy read CSV with a header.
- The module we need in this method is the CSV module with a CSV reader.
- First, we need to open the file with the open() function that gives us a file object.
- The file is then used for the CSV.reader which can be iterated over all rows returning for each row a list of the items as strings.
- It returns the header elements of a file in the form of a NumPy array.

**Example:**

```
import numpy as np
import csv
path = '/home/arvind/Documents/test.csv'
with open(path, 'r') as f:
reader = csv.reader(f, delimiter=',')
headers = next(reader)
data = np.array(list(reader)).astype(float)
print(headers)
```

Here is the Screenshot of following given code

Read: Python NumPy square

## Python NumPy read CSV file

- In this section, we will learn about
**NumPy read CSV files**. - To read CSV data into a record in a Numpy array you can use the Numpy library genfromtxt() function, In this functionâ€™s parameter, you need to set the delimiter to a comma.
- The genfromtxt() function is used quite frequently to load data from text files in Python.
- We can read data from CSV files using this function and store it into a NumPy array.

**Syntax:**

Here is the syntax of the genfromtxt() function

```
numpy.genfromtxt
(
fname
)
```

**Example:**

```
from numpy import genfromtxt
my_data = genfromtxt('/home/arvind/Documents/test.csv', delimiter=',')
print(my_data)
```

In the above example, we have stored the data in the variable my_data that will return the ndarray by passing the filename.

Here is the Screenshot of the following given code

Read: Python NumPy Array + Examples

## Python NumPy read CSV Shape

- In this section, we will learn about
**NumPy read CSV shape**. - The command data.shape will return a tuple that shows us the number of rows and columns in our numpy data array.
- The file is then used for the CSV.reader which can be iterated over all rows returning for each row a list of the items as strings.
- The output will (10,9) tells us that we have an array of data with 10 rows and 9 columns.

**Example:**

```
import numpy as np
import csv
path = '/home/arvind/Documents/test.csv'
with open(path, 'r') as f:
reader = csv.reader(f, delimiter=',')
headers = next(reader)
data = np.array(list(reader)).astype(float)
print(data.shape)
```

Here is the Screenshot of following given code

Read Python NumPy repeat

## Python NumPy read CSV into 2d NumPy array

- In this section, we will learn about
**NumPy read CSV into a 2d NumPy array**. - load.txt() and open() functions to load a CSV file into a 2Dimension NumPy Array. Call open file to open the CSV text file Use numpy.loadtxt( CSV file, delimiter) with the file as the result of the previous step and delimiter as “,” to return the data in a two-dimensional NumPy.
- Two Dimensional Numpy means the collection of homogenous data in lists of a list. It is also known as a matrix. In a 2D array, you have to use two square brackets that is why it said lists of lists.

**Example:**

```
import numpy as np
path = open('/home/arvind/Documents/app.csv')
array = np.loadtxt(path, delimiter=",",dtype='int')
print(array)
```

Here is the Screenshot of the following given code

Read: Python NumPy log

## Python NumPy read CSV pandas

- In this section, we will learn about
**NumPy read CSV pandas**. - CSV files contain plain text and are a well-known format that everyone can read, including Pandas.
**Pandas**is a python library that is used for data manipulation analysis and cleaning. Python pandas are well-suited for different kinds of data such as we can work on tabular data.- In this example first, we create a dataframe variable in which we have to read a CSV file

**Example:**

```
import numpy as np
import pandas as pd
df = pd.read_csv('/home/arvind/Documents/test.csv')
print(df)
```

Here is the Screenshot of following given code.

Read: Python Pandas CSV Tutorial

## Python NumPy read CSV skip_rows

- In this section, we will learn about
**NumPy read CSV skip_rows**. - If we pass skiprows argument as a tuple of ints, then it will skip the rows from CSV at specified indices.
- For example, if we want to skip lines at index 0,3 while reading the CSV file and initializing a NumPy array.
- We create a variable and use the NumPy module loadtxt in which passes the argument delimiters and skiprows.

**Example:**

```
#read input file
from numpy import loadtxt
file = open('/home/arvind/Documents/test.csv', 'rb')
data = loadtxt(file,delimiter = ",",skiprows=2)
print(data)
```

Here is the Screenshot of folllowing given code

Read: Python write a list to CSV

## Python NumPy read CSV Data type

- In this section, we will learn about
**NumPy read CSV Data type**. - First, we have to
**create a NumPy module**in which we have to pass an argument datatype. - In Python, NumPy contains values using its own types, which are distinct from Python types like float and integer.

**Example:**

```
import numpy as np
path = open('/home/arvind/Documents/app.csv')
array = np.loadtxt(path, delimiter=",",dtype='float')
print(array)
```

Here is the Screenshot of the following given code

Read: How to write Python array to CSV

## Python NumPy read CSV Size

- In this section, we will learn about
**NumPy read CSV size**. - It returns an int representing the number of elements in this object.
- It takes an argument
**ndarray.size**(number of elements in the array).

**Example:**

```
import numpy as np
import csv
path = '/home/arvind/Documents/test.csv'
with open(path, 'r') as f:
reader = csv.reader(f, delimiter=',')
headers = next(reader)
data = np.array(list(reader)).astype(float)
print(data.size)
```

Here is the Screenshot of following given code

You may like the following Python tutorials:

- Python Read CSV File and Write CSV File
- Python replace a string in a file
- Check if NumPy Array is Empty in Python
- Python Tkinter Colors + Example
- Python NumPy empty array

In this Python NumPy tutorial, we learned **Python NumPy read CSV** and also we will cover the below examples:

- Python NumPy read CSV with header
- Python NumPy read CSV file
- Python NumPy read CSV Shape
- Python NumPy read CSV into 2d NumPy array
- Python NumPy read CSV pandas
- Python NumPy read CSV skip_rows
- Python NumPy read CSV Data type
- Python NumPy read CSV Size

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.