Pandas: Data manipulation in Python ?????
Pandas is a popular open-source data manipulation library in Python. It is built on top of NumPy and provides easy-to-use data structures and data analysis tools for handling tabular data. Pandas is widely used in data science, data analysis, and machine learning projects. In this article, we will explore the key features of Pandas and learn how to use it for data manipulation.
Pandas Data Structures
Pandas provides two primary data structures: Series and DataFrame.
Series
A Series is a one-dimensional array-like object that can hold any data type. It is similar to a column in a spreadsheet or a SQL table. A Series consists of two arrays: one for the data and one for the index. The data can be a NumPy array, a Python list, a scalar value, or a dictionary.
Here’s an example of creating a Series:
data = [10, 20, 30, 40, 50]
index = ['a', 'b', 'c', 'd', 'e']
s = pd.Series(data, index=index)
print(s)
Output:
b 20
c 30
d 40
e 50
dtype: int64
DataFrame
A DataFrame is a two-dimensional tabular data structure with rows and columns. It is similar to a spreadsheet or a SQL table. A DataFrame consists of three components: the data, the index, and the columns. The data can be a NumPy array, a Python list of lists, a dictionary of dictionaries, or a Pandas Series.
Here’s an example of creating a DataFrame:
data = {
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
'age': [25, 32, 18, 47, 33],
'cou