Are you someone who works with data sets, but finds organizing and manipulating data tedious and time consuming? Look no further than Python! Python libraries such as Pandas and Numpy can make this process a breeze, once you learn how to use them effectively.

**Pandas Basics**
Pandas is one of the most popular Python libraries for data manipulation. It provides data structures for efficiently storing and manipulating large data sets, that can often be messy and unorganized. To begin with, you should have a basic understanding the following three data structures:

**Series**: A Series is a one-dimensional array-like data structure that holds any data type, such as integers or strings. It is essentially a column in a spreadsheet.**DataFrames**: A DataFrame is a two-dimensional table-like data structure consisting of rows and columns, similar to a spreadsheet. Each column in the DataFrame is a Series.**Panels**: A Panel is a three-dimensional data structure consisting of multiple DataFrames.

Now let’s dive in to some code examples for using Pandas:

```
import pandas as pd
df = pd.read_csv('my_data.csv') # read in a CSV file as a DataFrame
df.head() # prints the first five rows of data in the DataFrame
```

The `read_csv()`

function allows you to read in CSV files as dataframes, which can then easily be manipulated using functions such as `head()`

, which prints the first five rows of data.

**Data Manipulation with Pandas**
One of the most powerful features of Pandas is its ability to manipulate data. Here are just a few examples of built-in functions in Pandas that can be used for data manipulation:

```
# Renaming a column in a DataFrame
df.rename(columns={"old_col_name": "new_col_name"}, inplace=True) # inplace=True saves the changes in the original DataFrame
# Grouping data by a common value
df.groupby('column_name').sum() # returns the sum of all columns grouped by unique values in 'column_name'
# Selecting rows based on a certain condition
df[df['age'] > 25] # returns all rows where age is greater than 25
```

**Numpy Basics**
Numpy is another Python library that is popular in the realm of data manipulation. It is particularly useful for working with numerical data and provides a variety of built-in functions for processing data.

For example, below is some code for creating a Numpy array, performing basic math functions on that array, as well as indexing the array:

```
import numpy as np
my_array = np.array([[1, 2, 3], [4, 5, 6]]) # creates a 2D array
my_array + 1 # adds one to every element of the array
my_array[1,2] # returns the value of the element in the second row and third column
```

**Conclusion**
Python is an incredibly versatile language that can be used for a wide variety of tasks, including data manipulation. By using popular libraries such as Pandas and Numpy, organizing and manipulating large data sets can be much simpler. With some practice, you too can become an expert at using Python for data manipulation!