A Pandas DataFrame is a two-dimensional table-like structure in Python where data is arranged in rows and columns. It’s one of the most commonly used tools for handling data and makes it easy to organize, analyze and manipulate data. It can store different types of data such as numbers, text and dates across its columns. The main parts of a DataFrame are:

• Data: Actual values in the table.
• Rows: Labels that identify each row.
• Columns: Labels that define each data category.

In this article, we’ll see the key components of a DataFrame and see how to work with it to make data analysis easier and more efficient.

Creating a Pandas DataFrame

Pandas allows us to create a DataFrame from many data sources. We can create DataFrames directly from Python objects like lists and dictionaries or by reading data from external files like CSV, Excel or SQL databases.

Here are some ways by which we create a dataframe:

1. Creating DataFrame using a List

If we have a simple list of data, we can easily create a DataFrame by passing that list to the pd.DataFrame() function.

import pandas as pd
 
lst = ['Geeks', 'For', 'Geeks', 'is', 
            'portal', 'for', 'Geeks']

df = pd.DataFrame(lst)
print(df)

Output:

2. Creating DataFrame from dict of ndarray/lists

We can create a DataFrame from a dictionary where the keys are column names and the values are lists or arrays.

• All arrays/lists must have the same length.
• If an index is provided, it must match the length of the arrays.
• If no index is provided, Pandas will use a default range index (0, 1, 2, …).

import pandas as pd
 
data = {'Name':['Tom', 'nick', 'krish', 'jack'],
        'Age':[20, 21, 19, 18]}
 
df = pd.DataFrame(data)
 
print(df)

Output:

For more details refer to Creating a Pandas DataFrame.

Working With Rows and Columns in Pandas DataFrame

We can perform basic operations on rows/columns like selecting, deleting, adding and renaming.

1. Column Selection

In Order to select a column in Pandas DataFrame, we can either access the columns by calling them by their columns name.

import pandas as pd
 
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}
 
df = pd.DataFrame(data)
 
print(df[['Name', 'Qualification']])

Output:

2. Row Selection

Pandas provide unique methods for selecting rows from a Data frame. DataFrame.loc[] method is used for label-based selection

Here we’ll be using nba.csv dataset in below examples for better understanding.

import pandas as pd
 
data = pd.read_csv("/content/nba.csv", index_col ="Name")

first = data.loc["Avery Bradley"]
second = data.loc["R.J. Hunter"]
 
print(first, "\n\n\n", second)

Output:

For more Details refer to Dealing with Rows and Columns

Indexing and Selecting Data in Pandas DataFrame

Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame. It allows us to access subsets of data such as:

• Selecting all rows and some columns.
• Selecting some rows and all columns.
• Selecting a specific subset of rows and columns.

Indexing can also be known as Subset Selection.

1. Indexing a Dataframe using indexing operator [] 

The indexing operator [] is the basic way to select data in Pandas. We can use this operator to access columns from a DataFrame. This method allows us to retrieve one or more columns. The .loc and .iloc indexers also use the indexing operator to make selections.

In order to select a single column, we simply put the name of the column in-between the brackets.

import pandas as pd
 
data = pd.read_csv("/content/nba.csv", index_col ="Name")

first = data["Age"]
 
print(first)

Output:

2. Indexing a DataFrame using .loc[ ]

The .loc method is used to select data by label. This means it uses the row and column labels to access specific data points. .loc[] is versatile because it can select both rows and columns simultaneously based on labels.

In order to select a single row using .loc[], we put a single row label in a .loc function.

import pandas as pd
 
data = pd.read_csv("/content/nba.csv", index_col ="Name")

first = data.loc["Avery Bradley"]
second = data.loc["R.J. Hunter"]
 
print(first, "\n\n\n", second)

Output:

3. Indexing a DataFrame using .iloc[ ] 

The .iloc() method allows us to select data based on integer position. Unlike .loc[] (which uses labels) .iloc[] requires us to specify row and column positions as integers (0-based indexing).

In order to select a single row using .iloc[], we can pass a single integer to .iloc[] function.

import pandas as pd
 
data = pd.read_csv("/content/nba.csv", index_col ="Name")
 
row2 = data.iloc[3] 
 
print(row2)

Output:

For more Details refer

• Indexing and Selecting Data with Pandas
• Boolean Indexing in Pandas

Working with Missing Data

Missing Data can occur when no information is available for one or more items or for an entire row/column. In Pandas missing data is represented as NaN (Not a Number). Missing data can be problematic in real-world datasets where data is incomplete. Pandas provides several methods to handle such missing data effectively:

1. Checking for Missing Values using isnull() and notnull()

To check for missing values (NaN) we can use two useful functions:

1. isnull(): It returns True for NaN (missing) values and False otherwise.
2. notnull(): It returns the opposite, True for non-missing values and False for NaN values.

import pandas as pd
 
import numpy as np
 
dict = {'First Score':[100, 90, np.nan, 95],
        'Second Score': [30, 45, 56, np.nan],
        'Third Score':[np.nan, 40, 80, 98]}

df = pd.DataFrame(dict)
 
df.isnull()

Output:

2. Filling Missing Values using fillna(), replace() and interpolate()

In order to fill null values in a datasets, we use fillna(), replace() and interpolate() function these function replace NaN values with some value of their own. All these function help in filling a null values in datasets of a DataFrame. Interpolate() function is used to fill NA values in the dataframe but it uses various interpolation technique to fill the missing values rather than hard-coding the value.

import pandas as pd
 
import numpy as np
 
dict = {'First Score':[100, 90, np.nan, 95],
        'Second Score': [30, 45, 56, np.nan],
        'Third Score':[np.nan, 40, 80, 98]}
df = pd.DataFrame(dict)
 
df.fillna(0)

3. Dropping Missing Values using dropna()

If we want to remove rows or columns with missing data we can use the dropna() method. This method is flexible which allows us to drop rows or columns depending on the configuration.

import pandas as pd
 
import numpy as np
 
dict = {'First Score':[100, 90, np.nan, 95],
        'Second Score': [30, np.nan, 45, 56],
        'Third Score':[52, 40, 80, 98],
        'Fourth Score':[np.nan, np.nan, np.nan, 65]}
 
df = pd.DataFrame(dict)
   
df

Output:

Now we drop rows with at least one Nan value (Null value).

import pandas as pd
 
import numpy as np
 
dict = {'First Score':[100, 90, np.nan, 95],
        'Second Score': [30, np.nan, 45, 56],
        'Third Score':[52, 40, 80, 98],
        'Fourth Score':[np.nan, np.nan, np.nan, 65]}
df = pd.DataFrame(dict)
 
df.dropna()

Output:

For more Details refer to Working with Missing Data in Pandas.

Iterating over rows and columns

Iteration refers to the process of accessing each item one at a time. In Pandas, it means iterating through rows or columns in a DataFrame to access or manipulate the data. We can iterate over rows and columns to extract values or perform operations on each item.

1. Iterating Over Rows

There are several ways to iterate over the rows of a Pandas DataFrame and three common methods are:

1. iteritems()
2. iterrows()
3. itertuples()

Each method provides different ways to iterate over the rows which depends on our specific needs.

import pandas as pd
  
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
 

df = pd.DataFrame(dict)
 
print(df)

Output:

Here we apply iterrows() function in order to get a each element of rows.

import pandas as pd
  
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
 
df = pd.DataFrame(dict)

for i, j in df.iterrows():
    print(i, j)
    print()

Output:

2. Iterating Over Columns

In order to iterate over columns, we need to create a list of dataframe columns and then iterating through that list to pull out the dataframe columns.

import pandas as pd
   
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
df = pd.DataFrame(dict)
 
print(df)

Output:

Now here we iterate through columns in order to iterate through columns we first create a list of dataframe columns and then iterate through list.

columns = list(df)
 
for i in columns:
    print (df[i][2])

Output:

 For more Details refer to Iterating over rows and columns in Pandas DataFrame

DataFrame Methods for Working with Data

Pandas has a variety of methods for manipulating data in a DataFrame. Here's are some useful DataFrame methods:

Related Post

1. Python | Pandas Series
2. Python | Pandas Working With Text Data
3. Python | Pandas Working with Dates and Times
4. Python | Pandas Merging, Joining and Concatenating.