Pandas is an open-source library for data analysis and manipulation in Python. It provides fast, flexible and expressive data structures for working with relational and labelled data. Originally developed by Wes McKinney in 2008, it has become a foundational tool in modern data science and serves as a highly programmable analogue to spreadsheet software.
Key characteristics
NumPy foundation: Built on top of NumPy, it inherits highly optimised, array-based computational performance.
Label-driven alignment: Data are automatically aligned according to explicit row and column labels, thereby improving the reliability of calculations involving partially mismatched datasets.
Heterogeneous typing: Unlike strict numerical arrays, Pandas can accommodate mixed data types, including integers, strings, floats and booleans, within a single tabular structure.
Missing-data resilience: It provides native support for detecting, representing and handling missing values, such as NaN.
Core data structures
Series: A one-dimensional labelled array capable of holding any data type. In practical terms, it resembles a single column in a spreadsheet.
DataFrame: A two-dimensional tabular data structure with labelled rows and columns. It may be regarded as a collection of Series sharing a common index, analogous to a table in SQL or a worksheet in Excel.
Core features and capabilities
Robust input/output parsing: Pandas supports efficient reading and writing across multiple formats, including CSV, Excel, SQL databases, JSON and Parquet.
Advanced data cleaning: Built-in methods enable users to identify, filter and remove duplicates, and to impute missing values.
Flexible wrangling and reshaping: The library facilitates pivoting, melting, slicing and subsetting operations based on conditional logic.
High-performance merging: Relational operations such as inner, outer, left and right joins, as well as concatenation, can be executed in concise code.
Split-apply-combine (GroupBy): Data may be grouped by specified criteria and summarised using aggregate operations such as sums, averages and custom calculations.
Time-series functionality: Pandas provides specialised tools for handling dates, converting time zones, generating date ranges and resampling time-stamped data.
Integrated plotting: It offers Matplotlib-based plotting utilities, enabling the rapid visualisation of tabular data with the .plot() interface.
Time-series functionality and integrated plotting are not covered in this article.
Practical examples
Installation
Using pip:
pip install pandas
Using conda:
conda install pandas
Using poetry:
poetry add pandas
Verify the installation
The following script may be used to confirm that pandas has been installed correctly.
import pandas as pd
print(pd.__version__)
3.0.5
Creating DataFrame
from dictionary
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie'],
'age': [25, 30, 35],
'city': ['NYC', 'LA', 'Chicago']
})
print(df)
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
from dictionary using NumPy
import numpy as np
df_dict = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie'],
'age': np.linspace(25, 35, num=3, dtype=int),
'city': ['NYC', 'LA', 'Chicago']
})
print(df_dict)
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
from list of lists
df_list = pd.DataFrame(
[['Alice', 25, 'NYC' ], ['Bob', 30, 'LA'], ['Charlie', 35, 'Chicago']],
columns=['name', 'age', 'city']
)
print(df_list)
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
from ndarray
df_ndarray = pd.DataFrame(
np.array([['Alice', 25, 'NYC'], ['Bob', 30, 'LA'], ['Charlie', 35, 'Chicago']]),
columns=['name', 'age', 'city']
)
print(df_ndarray)
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
Data inspection and information
basic info
print(f"Shape: {df.shape}") # (rows, columns)
print(f"Column names: {df.columns.tolist()}")
print(f"Data types:\n{df.dtypes}\n")
print(f"DataFrame Info:\n{df.info()}\n")
Shape: (3, 3)
Column names: ['name', 'age', 'city']
Data types:
name str
age int64
city str
dtype: object
<class 'pandas.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 3 non-null str
1 age 3 non-null int64
2 city 3 non-null str
dtypes: int64(1), str(2)
memory usage: 204.0 bytes
DataFrame Info:
None
head and tail
print(f"First default number of rows:\n{df.head()}\n")
print(f"First 2 rows:\n{df.head(2)}\n")
print(f"Last default number of rows:\n{df.tail()}\n")
print(f"Last 2 rows:\n{df.tail(2)}")
First default number of rows:
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
First 2 rows:
name age city
0 Alice 25 NYC
1 Bob 30 LA
Last default number of rows:
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
Last 2 rows:
name age city
1 Bob 30 LA
2 Charlie 35 Chicago
descriptive statistics for numerical columns
print(df.describe())
age
count 3.0
mean 30.0
std 5.0
min 25.0
25% 27.5
50% 30.0
75% 32.5
max 35.0
descriptive statistics for all columns with transpose layout
print(df.describe(include='all').T)
count unique top freq mean std min 25% 50% 75% max
name 3 3 Alice 1 NaN NaN NaN NaN NaN NaN NaN
age 3.0 NaN NaN NaN 30.0 5.0 25.0 27.5 30.0 32.5 35.0
city 3 3 NYC 1 NaN NaN NaN NaN NaN NaN NaN
unique and missing values
print(f"Unique values in 'name':\n {df['name'].unique()}\n")
print(f"Null values:\n {df.isnull().sum()}\n")
Unique values in 'name':
<StringArray>
['Alice', 'Bob', 'Charlie']
Length: 3, dtype: str
Null values:
name 0
age 0
city 0
dtype: int64
Selecting data by columns and rows
select column - returns Series
print(df['name'])
0 Alice
1 Bob
2 Charlie
Name: name, dtype: str
select multiple columns
print(df[['name', 'age']])
name age
0 Alice 25
1 Bob 30
2 Charlie 35
select by position (iloc)
print("Select first 2 rows, first 2 columns (iloc):")
print(df.iloc[0:2, 0:2])
Select first 2 rows, first 2 columns (iloc):
name age
0 Alice 25
1 Bob 30
select by label (loc)
print("Select by label using loc:")
print(df.loc[0]) # First row
print(df.loc[0:1, ['name', 'age']]) # First 2 rows, specific columns
Select by label using loc:
name Alice
age 25
city NYC
Name: 0, dtype: object
name age
0 Alice 25
1 Bob 30
Filtering and conditional selection
simple filter
print(df[df['age'] > 25])
name age city
1 Bob 30 LA
2 Charlie 35 Chicago
multiple conditions with AND
print("Age > 25 AND city == 'LA':")
print(df[(df['age'] > 25) & (df['city'] == 'LA')])
Age > 25 AND city == 'LA':
name age city
1 Bob 30 LA
multiple conditions with OR
print("Age == 25 OR city == 'Chicago':")
print(df[(df['age'] == 25) | (df['city'] == 'Chicago')])
Age == 25 OR city == 'Chicago':
name age city
0 Alice 25 NYC
2 Charlie 35 Chicago
multiple values using list
print("City in ['NYC', 'Chicago']:")
print(df[df['city'].isin(['NYC', 'Chicago'])])
City in ['NYC', 'Chicago']:
name age city
0 Alice 25 NYC
2 Charlie 35 Chicago
string filtering
print("City starts with 'L':")
print(df[df['city'].str.startswith('L')])
City starts with 'L':
name age city
1 Bob 30 LA
filter using query method
print(df.query('age > 25'))
name age city
1 Bob 30 LA
2 Charlie 35 Chicago
Adding and modifying columns
It is good practice to avoid modifying the original DataFrame when adding, deleting or altering data. For this reason, a deep copy of the DataFrame is used in the examples that follow.
hard copy dataframe
df_copy = df.copy()
print(f"Copy of Original DataFrame: \n{df_copy}\n")
Copy of Original DataFrame:
name age city
0 Alice 25 NYC
1 Bob 30 LA
2 Charlie 35 Chicago
add new column
df_copy['age_next_year'] = df_copy['age'] + 1
print(df_copy)
name age city age_next_year
0 Alice 25 NYC 26
1 Bob 30 LA 31
2 Charlie 35 Chicago 36
modify existing column with lambda function
df_copy['age_group'] = df_copy['age'].apply(lambda x: 'Young' if x < 30 else 'Older')
print(df_copy)
name age city age_next_year age_group
0 Alice 25 NYC 26 Young
1 Bob 30 LA 31 Older
2 Charlie 35 Chicago 36 Older
conditional assignment (using np.where)
df_copy['status'] = np.where(df_copy['age'] > 25, 'Senior', 'Junior')
print(df_copy)
name age city age_next_year age_group status
0 Alice 25 NYC 26 Young Junior
1 Bob 30 LA 31 Older Senior
2 Charlie 35 Chicago 36 Older Senior
rename columns
df_copy = df_copy.rename(columns={'name': 'full_name', 'city': 'location'})
print(df_copy)
df_copy.rename(columns={'full_name': 'name', 'location': 'city'}, inplace=True)
print(df_copy)
Rename columns:
full_name age location age_next_year age_group status
0 Alice 25 NYC 26 Young Junior
1 Bob 30 LA 31 Older Senior
2 Charlie 35 Chicago 36 Older Senior
Rename columns back using inplace:
name age city age_next_year age_group status
0 Alice 25 NYC 26 Young Junior
1 Bob 30 LA 31 Older Senior
2 Charlie 35 Chicago 36 Older Senior
drop columns
df_dropped = df_copy.drop(columns=['city'])
print(df_dropped)
name age age_next_year age_group status
0 Alice 25 26 Young Junior
1 Bob 30 31 Older Senior
2 Charlie 35 36 Older Senior
Data cleaning and missing values
sample dataframe with missing values
df_missing = pd.DataFrame({
'name': ['Alice', None, 'Charlie', None],
'age': [25, 30, np.nan, 40],
'city': ['NYC', None, 'Chicago', 'LA']
})
print("DataFrame with missing values:")
print(df_missing)
DataFrame with missing values:
name age city
0 Alice 25.0 NYC
1 NaN 30.0 NaN
2 Charlie NaN Chicago
3 NaN 40.0 LA
check for missing values using info
print(df_missing.info())
<class 'pandas.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 2 non-null str
1 age 3 non-null float64
2 city 3 non-null str
dtypes: float64(1), str(2)
memory usage: 228.0 bytes
None
There are four records in the sample DataFrame. The 'name' column contains two non-null values, while the 'age' and 'city' columns contain three non-null values each.
print(f"Check for missing values:\n{df_missing.isnull()}\n")
print(f"Check for missing values:\n{df_missing.isnull().sum()}\n")
Check for missing values:
name age city
0 False False False
1 True False True
2 False True False
3 True False False
Check for missing values:
name 2
age 1
city 1
dtype: int64
drop rows with missing values
# Drop rows with any missing values
print(f"{df_missing.dropna()}\n")
# Drop rows where specific column is missing
print(df_missing.dropna(subset=['age']))
Drop rows with missing values:
name age city
0 Alice 25.0 NYC
Drop rows where 'age' is missing:
name age city
0 Alice 25.0 NYC
1 NaN 30.0 NaN
3 NaN 40.0 LA
fill missing values
# Fill missing values with 0
print(df_missing.fillna(0))
print()
# Fill missing values with forward fill
print(df_missing.ffill())
print()
# Fill with mean
df_missing['age'] = df_missing['age'].fillna(df_missing['age'].mean())
print(df_missing)
print()
name age city
0 Alice 25.0 NYC
1 0 30.0 0
2 Charlie 0.0 Chicago
3 0 40.0 LA
name age city
0 Alice 25.0 NYC
1 Alice 30.0 NYC
2 Charlie 30.0 Chicago
3 Charlie 40.0 LA
name age city
0 Alice 25.000000 NYC
1 NaN 30.000000 NaN
2 Charlie 31.666667 Chicago
3 NaN 40.000000 LA
Grouping and aggregation
sample dataframe
df_sales = pd.DataFrame({
'department': ['Sales', 'Sales', 'IT', 'IT', 'HR', 'HR'],
'employee': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],
'salary': [50000, 55000, 75000, 75000, 45000, 48000],
'bonus': [5000, 6000, 10000, 12000, 3000, 2000]
})
print(df_sales)
department employee salary bonus
0 Sales Alice 50000 5000
1 Sales Bob 55000 6000
2 IT Charlie 75000 10000
3 IT David 75000 12000
4 HR Eve 45000 3000
5 HR Frank 48000 2000
group by single column
print(df_sales.groupby('department')['salary'].mean())
department
HR 46500.0
IT 75000.0
Sales 52500.0
Name: salary, dtype: float64
group by and aggregate multiple columns
agg_result = df_sales.groupby('department').agg({
'salary': ['mean', 'sum', 'count'],
'bonus': 'mean'
})
print(agg_result)
salary bonus
mean sum count mean
department
HR 46500.0 93000 2 2500.0
IT 75000.0 150000 2 11000.0
Sales 52500.0 105000 2 5500.0
custom aggregation names
print(df_sales.groupby('department').agg(
avg_salary=('salary', 'mean'),
total_bonus=('bonus', 'sum'),
count=('employee', 'count')
))
avg_salary total_bonus count
department
HR 46500.0 5000 2
IT 75000.0 22000 2
Sales 52500.0 11000 2
group by multiple columns
df_sales['year'] = [2023, 2023, 2024, 2024, 2023, 2024]
print(df_sales.groupby(['department', 'year'])['salary'].mean())
department year
HR 2023 45000.0
2024 48000.0
IT 2024 75000.0
Sales 2023 52500.0
Name: salary, dtype: float64
Sorting and ranking
sort by single column ascending
print(df_sales.sort_values('salary'))
department employee salary bonus year
4 HR Eve 45000 3000 2023
5 HR Frank 48000 2000 2024
0 Sales Alice 50000 5000 2023
1 Sales Bob 55000 6000 2023
3 IT David 75000 12000 2024
2 IT Charlie 75000 10000 2024
sort by single column descending
print(df_sales.sort_values('salary', ascending=False))
department employee salary bonus year
3 IT David 75000 12000 2024
2 IT Charlie 75000 10000 2024
1 Sales Bob 55000 6000 2023
0 Sales Alice 50000 5000 2023
5 HR Frank 48000 2000 2024
4 HR Eve 45000 3000 2023
sort by multiple columns
print(df_sales.sort_values(['department', 'salary']))
department employee salary bonus year
4 HR Eve 45000 3000 2023
5 HR Frank 48000 2000 2024
2 IT Charlie 75000 10000 2024
3 IT David 75000 12000 2024
0 Sales Alice 50000 5000 2023
1 Sales Bob 55000 6000 2023
sort by index
# create a new DataFrame with 'employee' as the index
df_index = df_sales.set_index('employee')
print(df_index.sort_index())
department salary bonus year
employee
Alice Sales 50000 5000 2023
Bob Sales 55000 6000 2023
Charlie IT 75000 10000 2024
David IT 75000 12000 2024
Eve HR 45000 3000 2023
Frank HR 48000 2000 2024
ranking
df_sales['salary_rank'] = df_sales['salary'].rank(ascending=False)
print(df_sales[['employee', 'salary', 'salary_rank']])
employee salary salary_rank
0 Alice 50000 4.0
1 Bob 55000 3.0
2 Charlie 75000 1.5
3 David 75000 1.5
4 Eve 45000 6.0
5 Frank 48000 5.0
dense ranking
df_sales['salary_dense_rank'] = df_sales['salary'].rank(method='dense', ascending=False)
print(df_sales[['employee', 'salary', 'salary_dense_rank']])
employee salary salary_dense_rank
0 Alice 50000 3.0
1 Bob 55000 2.0
2 Charlie 75000 1.0
3 David 75000 1.0
4 Eve 45000 5.0
5 Frank 48000 4.0
Merging and joining DataFrames
sample dataframes df1, df2 and df3
df1 = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'department': ['Sales', 'IT', 'HR']
})
df2 = pd.DataFrame({
'id': [1, 2, 3],
'salary': [50000, 70000, 45000],
'bonus': [5000, 10000, 3000]
})
print(df1)
print()
print(df2)
id name department
0 1 Alice Sales
1 2 Bob IT
2 3 Charlie HR
id salary bonus
0 1 50000 5000
1 2 70000 10000
2 3 45000 3000
inner join (intersection)
print(pd.merge(df1, df2, on='id'))
id name department salary bonus
0 1 Alice Sales 50000 5000
1 2 Bob IT 70000 10000
2 3 Charlie HR 45000 3000
left join
df3 = pd.DataFrame({
'id': [1, 2, 4],
'salary': [50000, 70000, 80000]
})
print(pd.merge(df1, df3, on='id', how='left'))
id name department salary
0 1 Alice Sales 50000.0
1 2 Bob IT 70000.0
2 3 Charlie HR NaN
right join
print(pd.merge(df1, df3, on='id', how='right'))
id name department salary
0 1 Alice Sales 50000
1 2 Bob IT 70000
2 4 NaN NaN 80000
outer join (union)
print(pd.merge(df1, df3, on='id', how='outer'))
id name department salary
0 1 Alice Sales 50000.0
1 2 Bob IT 70000.0
2 3 Charlie HR NaN
3 4 NaN NaN 80000.0
concatenate vertically
print(pd.concat([df1, df1], ignore_index=True))
id name department
0 1 Alice Sales
1 2 Bob IT
2 3 Charlie HR
3 1 Alice Sales
4 2 Bob IT
5 3 Charlie HR
concatenate horizontally
print(pd.concat([df1, df2], axis=1))
id name department id salary bonus
0 1 Alice Sales 1 50000 5000
1 2 Bob IT 2 70000 10000
2 3 Charlie HR 3 45000 3000
Reshaping data using pivot tables
sample dataframe1
df_pivot = pd.DataFrame({
'product': ['A', 'A', 'B', 'B', 'C', 'C'],
'month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
'sales': [100, 150, 200, 250, 300, 350]
})
print(df_pivot)
product month sales
0 A Jan 100
1 A Feb 150
2 B Jan 200
3 B Feb 250
4 C Jan 300
5 C Feb 350
pivot table
pivot = df_pivot.pivot(index='product', columns='month', values='sales')
print(pivot)
month Feb Jan
product
A 150 100
B 250 200
C 350 300
sample dataframe2
df_pivot2 = pd.DataFrame({
'category': ['A', 'A', 'A', 'B', 'B', 'B'],
'region': ['North', 'South', 'North', 'South', 'North', 'South'],
'sales': [100, 150, 120, 200, 250, 300]
})
print(df_pivot2)
category region sales
0 A North 100
1 A South 150
2 A North 120
3 B South 200
4 B North 250
5 B South 300
pivot with aggregation
pivot_agg = df_pivot2.pivot_table(
index='category',
columns='region',
values='sales',
aggfunc='sum'
)
print(pivot_agg)
region North South
category
A 220 150
B 250 500
unstack
df_stacked = df_pivot.set_index(['product', 'month'])['sales']
df_unstacked = df_stacked.unstack()
print(df_stacked)
print()
print(df_unstacked)
product month
A Jan 100
Feb 150
B Jan 200
Feb 250
C Jan 300
Feb 350
Name: sales, dtype: int64
month Feb Jan
product
A 150 100
B 250 200
C 350 300
melt (unpivot)
df_melted = pd.melt(df_unstacked.reset_index(), id_vars=['product'], var_name='variable', value_name='value')
print(df_melted)
product variable value
0 A Feb 150
1 B Feb 250
2 C Feb 350
3 A Jan 100
4 B Jan 200
5 C Jan 300
String operations
sample dataframe
df_str = pd.DataFrame({
'name': ['alice smith', 'bob jones', 'charlie brown'],
'email': ['[email protected]', '[email protected]', '[email protected]']
})
print(df_str)
name email
0 alice smith [email protected]
1 bob jones [email protected]
2 charlie brown [email protected]
string upper, title case
print(df_str['name'].str.upper())
print()
print(df_str['name'].str.title())
print()
0 ALICE SMITH
1 BOB JONES
2 CHARLIE BROWN
Name: name, dtype: str
0 Alice Smith
1 Bob Jones
2 Charlie Brown
Name: name, dtype: str
string lenght
print(df_str['name'].str.len())
0 11
1 9
2 13
Name: name, dtype: int64
string contains
print(df_str[df_str['name'].str.contains('alice')])
extract part of string
print(df_str['name'].str.split(' ').str[0])
print()
print(df_str['name'].str.split(' ').str[1])
0 alice
1 bob
2 charlie
Name: name, dtype: object
0 smith
1 jones
2 brown
Name: name, dtype: object
replace part of string
print(df_str['name'].str.replace('smith', 'Smith'))
0 alice Smith
1 bob jones
2 charlie brown
Name: name, dtype: str
extract digits from string
df_codes = pd.DataFrame({'code': ['A123', 'B456', 'C789']})
print(df_codes)
print()
df_codes['code'] = df_codes['code'].str.replace(r'\D', '', regex=True)
print(df_codes)
code
0 A123
1 B456
2 C789
code
0 123
1 456
2 789
get domain from email address
print(df_str['email'].str.extract(r'@([a-z]+)\.com'))
0
0 email
1 email
2 email
Datetime operations
sample dataframe
df_dates = pd.DataFrame({
'date': ['2024-01-15', '2024-02-20', '2024-03-10'],
'value': [100, 150, 200]
})
print(df_dates)
print(df_dates.dtypes)
date value
0 2024-01-15 100
1 2024-02-20 150
2 2024-03-10 200
date str
value int64
dtype: object
convert to datetime type
df_dates['date'] = pd.to_datetime(df_dates['date'])
print(df_dates)
print(df_dates.dtypes)
date value
0 2024-01-15 100
1 2024-02-20 150
2 2024-03-10 200
date datetime64[us]
value int64
dtype: object
extract components
df_dates['year'] = df_dates['date'].dt.year
df_dates['month'] = df_dates['date'].dt.month
df_dates['day'] = df_dates['date'].dt.day
df_dates['day_name'] = df_dates['date'].dt.day_name()
print(df_dates)
date value year month day day_name
0 2024-01-15 100 2024 1 15 Monday
1 2024-02-20 150 2024 2 20 Tuesday
2 2024-03-10 200 2024 3 10 Sunday
date add 10 days
print(df_dates['date'] + pd.Timedelta(days=10))
0 2024-01-25
1 2024-03-01
2 2024-03-20
Name: date, dtype: datetime64[us]
date range 5 days
date_range = pd.date_range(start='2024-01-01', periods=5, freq='D')
print(date_range)
DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',
'2024-01-05'],
dtype='datetime64[us]', freq='D')
filter by date
start_date = pd.to_datetime('2024-02-01')
end_date = pd.to_datetime('2024-03-01')
df_filtered = df_dates[(df_dates['date'] >= start_date) & (df_dates['date'] <= end_date)]
print(df_filtered)
date value year month day day_name
1 2024-02-20 150 2024 2 20 Tuesday
Mathematical and statistical operations
sample dataframe
df_math = pd.DataFrame({
'A': [10, 20, 30, 40, 50],
'B': [5, 10, 15, 20, 25],
'C': [1, 2, 3, 4, 5]
})
print(df_math)
A B C
0 10 5 1
1 20 10 2
2 30 15 3
3 40 20 4
4 50 25 5
basic statistics
print(f"Mean: {df_math['A'].mean()}")
print(f"Median: {df_math['A'].median()}")
print(f"Std Dev: {df_math['A'].std()}")
print(f"Min: {df_math['A'].min()}")
print(f"Max: {df_math['A'].max()}")
print(f"Sum: {df_math['A'].sum()}")
Mean: 30.0
Median: 30.0
Std Dev: 15.811388300841896
Min: 10
Max: 50
Sum: 150
percentiles
print(f"25th percentile: {df_math['A'].quantile(0.25)}")
print(f"75th percentile: {df_math['A'].quantile(0.75)}")
25th percentile: 20.0
75th percentile: 40.0
correlation
print(df_math.corr())
A B C
A 1.0 1.0 1.0
B 1.0 1.0 1.0
C 1.0 1.0 1.0
covariance
print(df_math.cov())
A B C
A 250.0 125.0 25.0
B 125.0 62.5 12.5
C 25.0 12.5 2.5
arithmetic operations
df_math['A_add_B'] = df_math['A'] + df_math['B']
df_math['A_subtract_B'] = df_math['A'] - df_math['B']
df_math['A_squared'] = df_math['A'] ** 2
print(df_math)
A B C A_add_B A_subtract_B A_squared
0 10 5 1 15 5 100
1 20 10 2 30 10 400
2 30 15 3 45 15 900
3 40 20 4 60 20 1600
4 50 25 5 75 25 2500
apply numpy function
print(df_math['A'].apply(np.sqrt))
0 3.162278
1 4.472136
2 5.477226
3 6.324555
4 7.071068
Name: A, dtype: float64
cumulative operations
print(f"Cumulative sum of A:\n{df_math['A'].cumsum()}\n")
print(f"Cumulative product of A:\n{df_math['A'].cumprod()}\n")
Cumulative sum of A:
0 10
1 30
2 60
3 100
4 150
Name: A, dtype: int64
Cumulative product of A:
0 10
1 200
2 6000
3 240000
4 12000000
Name: A, dtype: int64
Useful tips and best practices
Create a copy to avoid accidental overwriting
df_copy = df.copy()
use inplace carefully
df_inplace = pd.DataFrame({'A': [1, 2, 3]})
df_inplace.sort_values('A', inplace=True)
Vectorised operations are generally faster than apply/loops
import time
df_large = pd.DataFrame({'A': range(10000)})
# Slow: using apply
start = time.time()
result_slow = df_large['A'].apply(lambda x: x * 2)
time_slow = time.time() - start
# Fast: vectorized
start = time.time()
result_fast = df_large['A'] * 2
time_fast = time.time() - start
print(f" Apply time: {time_slow:.6f}s")
print(f" Vectorized time: {time_fast:.6f}s")
✓ Vectorized operations are faster
Apply time: 0.010587s
Vectorized time: 0.000676s
use groupby instead of loops
df_grouped = pd.DataFrame({
'group': ['A', 'A', 'B', 'B'],
'value': [1, 2, 3, 4]
})
result = df_grouped.groupby('group')['value'].sum()
print(result)
✓ Use groupby for group operations (not loops)
group
A 3
B 7
Name: value, dtype: int64
For memory efficiency, categorical values may be preferable for repeated string data
df_mem = pd.DataFrame({'category': ['A', 'B', 'A', 'B'] * 1000})
print(f"✓ Use categorical for repeated string values")
print(f" Object dtype: {df_mem['category'].memory_usage(deep=True)} bytes")
df_mem['category'] = df_mem['category'].astype('category')
print(f" Categorical: {df_mem['category'].memory_usage(deep=True)} bytes")
✓ Use categorical for repeated string values
Object dtype: 200132 bytes
Categorical: 4232 bytes
chaining operations
result = (df
.query('age > 25')
.copy()
.assign(age_group='Senior')
.sort_values('age', ascending=False)
)
print(result)
name age city age_group
2 Charlie 35 Chicago Senior
1 Bob 30 LA Senior
Conclusion
Pandas is one of the most practical and widely used libraries for data analysis in Python. This tutorial has introduced the core ideas behind creating and inspecting DataFrames, selecting and filtering data, cleaning missing values, grouping observations, and combining datasets from different sources.
A major strength of Pandas lies in its ability to make data manipulation both readable and efficient. With a small set of intuitive methods, it becomes straightforward to transform raw data into a structure suitable for exploration, reporting, or further analysis. These capabilities are especially valuable in real-world workflows where datasets are often messy, incomplete, or spread across multiple files.
To build confidence with Pandas, regular practice is essential. Readers are encouraged to experiment with the methods shown here on their own datasets, compare different approaches for the same task, and gradually explore more advanced features such as time-series handling, custom aggregations, and multi-index operations. In this way, the foundations introduced in this article can grow into a deeper and more flexible understanding of data analysis in Python.
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