Basic Operations in Pandas After Loading Data

Last Updated: 29th August 2025

Here We will learn some of the most useful functions in Pandas.They are very useful when working with dataframes and series because they provide a lot of information about the dataframe or series.

Let Data is like this

import pandas as pd
import pandas as pd

df = pd.DataFrame({
    "Name": ["A", "B", "C","D","E","F"],
    "Age": [20, 21, None, 22, 23, 24],
    "Marks": [85, 90, 95, 80, None, 70]
})

📊 head() & tail(): Get the first and last 5 rows of the DataFrame.

df.head()   # First 5 rows
df.head(2)  # First 2 rows
df.tail()   # Last 5 rows
df.tail(2)  # Last 2 rows

📊 shape: Get the number of rows and columns in the DataFrame.

df.shape

0️⃣ columns: Get the column names of the DataFrame.

df.columns

🔍 dtypes: Get the data types of each column in the DataFrame.

df.dtypes

🔍 info(): Get a summary of the DataFrame, including column names, data types,memory usage and non-null counts.

df.info()

📊 describe(): Give count, mean, std, min, max, etc.

# Summary stats of numeric columns
df.describe()

# Include non-numeric also
df.describe(include="all")

📈 value_counts(): Get counts of unique values in a column.

df["Age"].value_counts()

🆔 unique(): Get unique values in a column.

df["Name"].unique()

🆔 nunique(): Get count of unique values in a column

df["Name"].nunique()

📊 isnull(): Returns True where values are missing, else False.

df.isnull()

📊 isna(): Returns True where values are missing, else False.

df.isna()

📊 notnull(): Returns True where values are not missing, else False.

df.notnull()

📊 notna(): Returns True where values are not missing, else False.

df.notna()
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