Python Pandas - Basic Functionality

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Pandas is a powerful data manipulation library in Python, providing essential tools to work with data in both Series and DataFrame formats. These two data structures are crucial for handling and analyzing large datasets.

Understanding the basic functionalities of Pandas, including its attributes and methods, is essential for effectively managing data, these attributes and methods provide valuable insights into your data, making it easier to understand and process. In this tutorial you will learn about the basic attributes and methods in Pandas that are crucial for working with these data structures.

Working with Attributes in Pandas

Attributes in Pandas allow you to access metadata about your Series and DataFrame objects. By using these attributes you can explore and easily understand the data.

Series and DataFrame Attributes

Following are the widely used attribute of the both Series and DataFrame objects −

Example

Let's create a Pandas Series and explore these attributes operation.

import pandas as pd
import numpy as np

# Create a Series with random numbers
s = pd.Series(np.random.randn(4))

# Exploring attributes
print("Data type of Series:", s.dtype)
print("Index of Series:", s.index)
print("Values of Series:", s.values)
print("Shape of Series:", s.shape)
print("Number of dimensions of Series:", s.ndim)
print("Size of Series:", s.size)
print("Is Series empty?:", s.empty)

Its output is as follows −

Data type of Series: float64
Index of Series: RangeIndex(start=0, stop=4, step=1)
Values of Series: [-1.02016329  1.40840089  1.36293022  1.33091391]
Shape of Series: (4,)
Number of dimensions of Series: 1
Size of Series: 4
Is Series empty?: False

Example

Let's look at below example and understand working of these attributes on a DataFrame object.

import pandas as pd
import numpy as np

# Create a DataFrame with random numbers
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
print("DataFrame:")
print(df)

print("Results:")
print("Data types:", df.dtypes)
print("Index:", df.index)
print("Columns:", df.columns)
print("Values:")
print(df.values)
print("Shape:", df.shape)
print("Number of dimensions:", df.ndim)
print("Size:", df.size)
print("Is empty:", df.empty)

On executing the above code you will get the following output −

DataFrame:
          A         B         C         D
0  2.161209 -1.671807 -1.020421 -0.287065
1  0.308136 -0.592368 -0.183193  1.354921
2 -0.963498 -1.768054 -0.395023 -2.454112

Results:
Data types: 
A    float64
B    float64
C    float64
D    float64
dtype: object
Index: RangeIndex(start=0, stop=3, step=1)
Columns: Index(['A', 'B', 'C', 'D'], dtype='object')
Values:
[[ 2.16120893 -1.67180742 -1.02042138 -0.28706468]
 [ 0.30813618 -0.59236786 -0.18319262  1.35492058]
 [-0.96349817 -1.76805364 -0.3950226  -2.45411245]]
Shape: (3, 4)
Number of dimensions: 2
Size: 12
Is empty: False

Exploring Basic Methods in Pandas

Pandas offers several basic methods in both the data structures, that makes it easy to quickly look at and understand your data. These methods help you get a summary and explore the details without much effort.

Series and DataFrame Methods

Example

Let us now create a Series and see the working of the Series basic methods.

import pandas as pd
import numpy as np

# Create a Series with random numbers
s = pd.Series(np.random.randn(10))

print("Series:")
print(s)

# Using basic methods
print("First 5 elements of the Series:\n", s.head())
print("\nLast 3 elements of the Series:\n", s.tail(3))
print("\nDescriptive statistics of the Series:\n", s.describe())

Its output is as follows −

Series:
0   -0.295898
1   -0.786081
2   -1.189834
3   -0.410830
4   -0.997866
5    0.084868
6    0.736541
7    0.133949
8    1.023674
9    0.669520
dtype: float64
First 5 elements of the Series:
 0   -0.295898
1   -0.786081
2   -1.189834
3   -0.410830
4   -0.997866
dtype: float64

Last 3 elements of the Series:
 7    0.133949
8    1.023674
9    0.669520
dtype: float64

Descriptive statistics of the Series:
 count    10.000000
mean     -0.103196
std       0.763254
min      -1.189834
25%      -0.692268
50%      -0.105515
75%       0.535627
max       1.023674
dtype: float64

Example

Now look at below example and understand working of the basic methods on a DataFrame object.

import pandas as pd
import numpy as np

#Create a Dictionary of series
data = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
   'Age':pd.Series([25,26,25,23,30,29,23]), 
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
 
#Create a DataFrame
df = pd.DataFrame(data)
print("Our data frame is:\n")
print(df)

# Using basic methods
print("\nFirst 5 rows of the DataFrame:\n", df.head())
print("\nLast 3 rows of the DataFrame:\n", df.tail(3))
print("\nInfo of the DataFrame:")
df.info()
print("\nDescriptive statistics of the DataFrame:\n", df.describe())

On executing the above code you will get the following output −

Our data frame is:

    Name  Age  Rating
0    Tom   25    4.23
1  James   26    3.24
2  Ricky   25    3.98
3    Vin   23    2.56
4  Steve   30    3.20
5  Smith   29    4.60
6   Jack   23    3.80

First 5 rows of the DataFrame:
     Name  Age  Rating
0    Tom   25    4.23
1  James   26    3.24
2  Ricky   25    3.98
3    Vin   23    2.56
4  Steve   30    3.20

Last 3 rows of the DataFrame:
     Name  Age  Rating
4  Steve   30     3.2
5  Smith   29     4.6
6   Jack   23     3.8

Info of the DataFrame:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7 entries, 0 to 6
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    7 non-null      object 
 1   Age     7 non-null      int64  
 2   Rating  7 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 296.0+ bytes

Descriptive statistics of the DataFrame:
              Age    Rating
count   7.000000  7.000000
mean   25.857143  3.658571
std     2.734262  0.698628
min    23.000000  2.560000
25%    24.000000  3.220000
50%    25.000000  3.800000
75%    27.500000  4.105000
max    30.000000  4.600000