Pandas: DataFrame Operations
Turning Your Data Into a Well-Organized Chaos!
Pandas DataFrame Analysis
View and analyze your data frames with built-in Pandas methods. Stats made simple!
Example 1: Get a quick statistical summary.
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
print(df.describe())
# Output:
# A B
# count 3.0 3.0
# mean 2.0 5.0
# std 1.0 1.0
# min 1.0 4.0
# 25% 1.5 4.5
# 50% 2.0 5.0
# 75% 2.5 5.5
# max 3.0 6.0
Example 2: Calculate column-wise means.
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
print(df.mean())
# Output:
# A 2.0
# B 5.0
# dtype: float64
Example 3: Return the first n rows in a data frame.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
print(df.head(3))
# Output:
# Name Age City
# 0 John 25 New York
# 1 Alice 30 Paris
# 2 Bob 35 London
Example 4: Return the last n rows in a data frame.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
print(df.tail(3))
# Output:
# Name Age City
# 2 Bob 35 London
# 3 Emma 28 Sydney
# 4 Mike 32 Tokyo
Example 5: Get the data frame information.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
print(df.info())
# Output:
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 5 entries, 0 to 4
# Data columns (total 3 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 Name 5 non-null object
# 1 Age 5 non-null int64
# 2 City 5 non-null object
# dtypes: int64(1), object(2)
# memory usage: 248.0+ bytes
# None
Pandas DataFrame Manipulation
Add, update, or drop columns and rows with ease.
Example 1: Add and drop columns.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
df["Country"] = ["United States", "France", "United Kingdom", "Australia", "Japan"]
print(df.head(2))
print("================")
df.drop("Age", axis=1, inplace=True)
# To drop multiple: df.drop(["Age", "City"], axis=1, inplace=True)
# Using columns: df.drop(columns="Age", inplace=True)
print(df.head(2))
# Name Age City Country
# 0 John 25 New York United States
# 1 Alice 30 Paris France
# ================
# Name City Country
# 0 John New York United States
# 1 Alice Paris France
Example 2: Insert and drop rows.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
df.loc[len(df.index)] = ["Drake", 32, "Bangkok"]
# To replace: df.loc[2] = ["Drake", 32, "Bangkok"]
print(df.tail(2))
print("================")
df.drop(1, axis=0, inplace=True)
# To drop multiple: df.drop([1, 3], axis=0, inplace=True)
# Using index: df.drop(index=1, inplace=True)
print(df.head(2))
# Name Age City
# 4 Mike 32 Tokyo
# 5 Drake 32 Bangkok
# ================
# Name Age City
# 0 John 25 New York
# 2 Bob 35 London
Example 3: Rename column names and indexes.
data = {
"Name": ["John", "Alice", "Bob", "Emma", "Mike"],
"Age": [25, 30, 35, 28, 32],
"City": ["New York", "Paris", "London", "Sydney", "Tokyo"],
}
df = pd.DataFrame(data)
df.rename(columns={"City": "Address"}, inplace=True)
# Using mapper: df.rename(mapper={"City": "Address"}, axis=1, inplace=True)
print(df.head(2))
print("================")
df.rename(index={1: 100}, inplace=True)
# Using mapper: df.rename(mapper={1: 100}, axis=0, inplace=True)
print(df.head(2))
# Name Age Address
# 0 John 25 New York
# 1 Alice 30 Paris
# ================
# Name Age Address
# 0 John 25 New York
# 100 Alice 30 Paris
Think of it like playing Tetris but with data.
Pandas Indexing and Slicing
Access data with labels or positions—no guessing required.
Example 1: Indexing by column or row.
df = pd.DataFrame({'A': [10, 20], 'B': [30, 40]})
print(df['A']) # Access column
print(df.iloc[0]) # Access first row by position
print(df.loc[0]) # Access first row by label
Example 2: Slice rows and columns.
print(df.iloc[:1]) # First row
print(df[['A', 'B']]) # Selected columns
Indexing in Pandas is like peeling an onion: layer by layer.
Pandas Select
Filter specific rows or data based on conditions.
Example 1: Simple condition.
print(df[df['A'] > 15])
Example 2: Multiple conditions.
print(df[(df['A'] > 10) & (df['B'] < 40)])
It’s like swiping right on the rows you love.
Pandas Multiindex
For when a single index just isn’t enough.
Example: Create and use a MultiIndex.
arrays = [['A', 'A', 'B'], [1, 2, 1]]
index = pd.MultiIndex.from_arrays(arrays, names=('Letter', 'Number'))
df = pd.DataFrame({'Value': [10, 20, 30]}, index=index)
print(df)
MultiIndex is for the overachievers. You know who you are.
Pandas Reshape
Reshape your data with melt and pivot.
Example 1: Use melt for long format.
df = pd.DataFrame({'ID': [1, 2], 'Value': [10, 20]})
melted = pd.melt(df, id_vars='ID')
print(melted
Example 2: Use pivot for wide format.
pivoted = melted.pivot(index='ID', columns='variable', values='value')
print(pivoted)
Shape your data like a pro.
Pandas Duplicate Values
Find and remove duplicate rows. Because no one likes redundancy.
Example 1: Detect duplicates.
df = pd.DataFrame({'A': [1, 1, 2], 'B': [3, 3, 4]})
print(df.duplicated())
Example 2: Drop duplicates.
df.drop_duplicates(inplace=True)
print(df
Duplicates, begone!
Pandas Pivot
Reorganize your data with pivot.
Example: Pivot data.
data = {'Date': ['2024-01-01', '2024-01-02'], 'Value': [10, 20]}
df = pd.DataFrame(data)
pivoted = df.pivot(index='Date', columns='Value')
print(pivoted)
Pivots make data feel fancy.
Pandas Pivot Table
Summarize data with a pivot table.
Example: Create a pivot table.
data = {'Category': ['A', 'A', 'B'], 'Value': [10, 20, 30]}
df = pd.DataFrame(data)
pivot_table = df.pivot_table(values='Value', index='Category', aggfunc='sum')
print(pivot_table)
You’re basically at Excel-level now.
