Ch 3Data Handling using Pandas – II — Class 12 Informatics Practices, concept-first.

As discussed in the previous chapter, Pandas is a well-established Python library used for manipulating, processing, and analysing data.

Descriptive statistics are the methods we use to get a basic understanding of a dataset — to summarise it rather than look at every single value.

The max() method in Pandas returns the largest value from a DataFrame. It works on all data types — for numbers it gives the highest numeric value, and for strings it gives the alphabetically last nam…

The min() method on a DataFrame returns the smallest value from each column (or each row, if you specify the axis).

The sum() method in Pandas is used to calculate totals from a DataFrame. When called on the entire DataFrame without any column filter, it returns the sum of every column — including text columns.

DataFrame.count() is the method used to find out how many non-null values exist in a DataFrame. It does not count missing (NaN) entries — only actual data.

The mean() method in Pandas calculates the average of numeric values. When called on a DataFrame without any arguments, it returns the mean of every numeric column.

The median() function in Pandas returns the middle value of a dataset. For a DataFrame, calling .median() gives you the median of each numeric column individually.

The mode is the value that appears most frequently in a dataset. While the mean and median give you a central tendency based on calculation or position, the mode tells you which value is most common —…

The quantile() method on a DataFrame is the tool for calculating quartiles. A quartile divides a sorted dataset into four equal parts.

Variance measures how spread out a set of numbers is. In Pandas, the .var() method on a DataFrame or Series calculates this spread.

The standard deviation tells you how spread out the numbers in a dataset are from their average (mean).

Aggregation is the process of taking a dataset — a column, a set of columns, or the whole DataFrame — and reducing it to a single numeric value.

Sorting means arranging data elements in a specified order — either ascending (smallest to largest) or descending (largest to smallest).

The groupby() function in Pandas is used to split a DataFrame into groups based on some criterion — typically the values in one or more columns.

The index of a DataFrame is the set of row labels used to access and retrieve data quickly. By default, Pandas assigns a numeric index starting from 0, as shown in the sample DataFrame of student mark…

In this section, we learn more techniques and functions that can be used to manipulate and analyse data in a DataFrame.

The way a dataset is arranged into rows and columns is referred to as the shape of the data. Reshaping data means changing that shape — rearranging the dataset — to make it suitable for particular ana…

A DataFrame can contain many rows, where each row holds values for different columns (attributes). When a value for a particular column is absent, it is called a missing value.

Real-world data is often incomplete. A student might be absent for a test, a sensor might fail to record a reading, or a survey question might be left blank.

When a dataset contains missing values (shown as NaN in Pandas), you have two broad choices: either drop the rows that contain those missing values, or replace them with some other value.

Missing values in a dataset are a loss of information. Instead of simply dropping those rows, you can fill them with an estimated or approximated value.

To work with real-world data, you will rarely type every value into a DataFrame by hand. Instead, data usually lives in files (like CSV or text files) or inside a database.

The core idea is simple: you take a table that lives inside a MySQL database and pull it into your Python environment as a pandas DataFrame.

Exporting data from Pandas to MySQL means writing the contents of a pandas DataFrame into a table inside a MySQL database.

- Descriptive statistics are used to quantitatively summarise a dataset. - Pandas provides many statistical functions for analysing data, including max(), min(), mean(), median(), mode(), std(), and v…

Questions from official CBSE sample papers.