Basic Functionalities of a Data Object

Whether you're working with a Series or a DataFrame, Pandas gives you a common set of built-in functionalities to quickly inspect, summarise, and understand your data before doing any real analysis.

Taking a First Look

import pandas as pd

df = pd.read_csv("students.csv")

print(df.head(3))    # first 3 rows
print(df.tail(3))    # last 3 rows
print(df.sample(2))  # 2 random rows

Structural Information

print(df.shape)    # (rows, columns)
print(df.info())   # column names, non-null counts, dtypes
print(df.dtypes)   # data type of each column

Statistical Summary

print(df.describe())     # count, mean, std, min, max for numeric columns
print(df["Score"].mean())
print(df["Score"].max())
print(df["Score"].value_counts())   # frequency of each unique value

Sorting Data

print(df.sort_values("Score", ascending=False))
print(df.sort_index())

Coming Up Next

Next, you'll learn how to combine data from multiple DataFrames using merging.