pandas Explained: Series and DataFrame

After completing this topic, you will be able to:

Explain what Series and DataFrame are in pandas.
Load a CSV file and perform basic operations.

What is pandas?

pandas is a library for working with tabular data in Python. Think of it as a way to manipulate spreadsheets like Excel, but with code.

python

import pandas as pd

# Read a CSV file – it's as simple as this
df = pd.read_csv("students.csv")
print(df)

text

Name  Age  Score
0  Kim  25     85
1  Lee  23     92
2  Park  27     78

pd is the conventional abbreviation for pandas. You'll almost always see it used in data analysis code.

Series: A One-Dimensional Data Structure

A Series is a labeled, one-dimensional array. A column in a DataFrame is a Series.

python

import pandas as pd

# Create a Series
scores = pd.Series([85, 92, 78], index=["Kim", "Lee", "Park"])
print(scores)

text

Kim     85
Lee     92
Park    78
dtype: int64

python

# Access by index
print(scores["Lee"])      # 92

# Conditional filtering
print(scores[scores >= 80])
# Kim     85
# Lee     92

# Operations
print(scores.mean())       # 85.0
print(scores.max())        # 92

It's similar to a list, but you can access elements by name, and it has built-in statistical methods.

DataFrame: The Whole Table

A DataFrame is a two-dimensional table composed of multiple Series. It has rows and columns.

python

import pandas as pd

# Create from a dictionary
data = {
    "Name": ["Kim", "Lee", "Park"],
    "Age": [25, 23, 27],
    "Score": [85, 92, 78]
}
df = pd.DataFrame(data)
print(df)

text

Name  Age  Score
0  Kim   25     85
1  Lee   23     92
2  Park  27     78

Basic Operations

python

# Select a column
print(df["Name"])           # Returns a Series
print(df[["Name", "Score"]])  # Returns a DataFrame

# Select a row
print(df.loc[0])            # Row 0 (by label)
print(df.iloc[0])           # Row 0 (by position)

# Conditional filtering
high = df[df["Score"] >= 80]
print(high)
#   Name  Age  Score
# 0  Kim   25     85
# 1  Lee   23     92

# Basic statistics
print(df.describe())
# Mean, standard deviation, min/max, etc., for Age and Score

Commonly Used Methods

python

df.head(3)          # First 3 rows
df.tail(3)          # Last 3 rows
df.shape            # (Number of rows, number of columns) - e.g., (3, 3)
df.columns          # List of column names
df.dtypes           # Data type of each column
df.info()           # Summary information (including missing values)
df.sort_values("Score", ascending=False)  # Sort by Score in descending order

Adding and Deleting Columns

python

# Add a new column
df["Passed"] = df["Score"] >= 80
print(df)
#   Name  Age  Score  Passed
# 0  Kim   25     85    True
# 1  Lee   23     92    True
# 2  Park  27     78   False

# Delete a column
df = df.drop("Passed", axis=1)
# axis=0 is for rows, axis=1 is for columns

Why pandas?

Task	Pure Python	pandas
Read CSV	10+ lines (open, split, loop)	`pd.read_csv()` 1 line
Calculate mean	Calculate `sum()/len()` directly	`df.mean()`
Conditional filtering	`for` + `if`	`df[df["column"] > value]`
Sorting	`sorted()` + `key`	`df.sort_values()`

Not only is the code shorter, but pandas is internally optimized with C/Cython, making it 10 to 100 times faster than pure Python.

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What is pandas — Series and DataFrame

pandas Explained: Series and DataFrame

After completing this topic, you will be able to:

What is pandas?

Series: A One-Dimensional Data Structure

DataFrame: The Whole Table

Basic Operations

Commonly Used Methods

Adding and Deleting Columns

Why pandas?

💬 Questions & Comments