- Handles missing data (NaN, NA, NaT easily).
- Flexible size mutability (insert/delete columns).
- Automatic data alignment (aligns labels automatically).
- Powerful
groupbyengine (split-apply-combine operations). - Easy data conversion (converts structures to DataFrames).
- Smart indexing (label-based slicing and subsetting).
- Intuitive data merging (easy joining of datasets).
- Flexible reshaping (pivoting and restructuring data).
- Hierarchical axis labeling (multiple labels per tick).
- Robust I/O tools (loads CSV, Excel, HDF5).
- Time-series ready (date shifting and frequency conversion).
- Series: One-dimensional array-like structure.
- DataFrame: Two-dimensional, size-mutable tabular structure.
1.
import pandas as pd
s = pd.Series([1, 3, 5, 7, 9])
print(s)
s = pd.Series([1, 3, 5, 7, 9])
print(s)
0 1 1 3 2 5 3 7 4 9 dtype: int64
2.
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
'Age': [28, 34, 29, 32]}
df = pd.DataFrame(data)
print(df)
Name Age 0 John 28 1 Anna 34 2 Peter 29 3 Linda 32
3.
# Creating a DataFrame from a dictionary. import pandas as pd data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Paris', 'London'] } df = pd.DataFrame(data) print(df)
4. # To look at the first few rows of a DataFrame.
print(df.head()) # First five rows
Name Age City
0 Alice 25 New York
1 Bob 30 Paris
2 Charlie 35 London
5.
# To access a column in the DataFrame.
ages = df['Age']
print(ages)
print(df['Name']) # Displays the 'Name' column
0 25
1 30
2 35
Name: Age, dtype: int64
0 Alice
1 Bob
2 Charlie
Name: Name, dtype: object
6.
subset = df[0:2] # First two rows
print(subset)
Name Age City
0 Alice 25 New York
1 Bob 30 Paris
7.
filtered_df = df[df['Age'] > 30] # Rows where age is greater than 30
print(filtered_df)
Name Age City
2 Charlie 35 London
8.
df['AgeInTenYears'] = df['Age'] + 10
print(df)
Name Age City AgeInTenYears
0 Alice 25 New York 35
1 Bob 30 Paris 40
2 Charlie 35 London 45
9.
print(df.describe())
Age AgeInTenYears
count 3.0 3.0
mean 30.0 40.0
std 5.0 5.0
min 25.0 35.0
25% 27.5 37.5
50% 30.0 40.0
75% 32.5 42.5
max 35.0 45.0
10.
f.rename(columns={'AgeYears': 'Age'}, inplace=True)
#print(df['Age'].mean()) # Average age
11.
df
print(df['Age'].mean()) # Average age
30.0
12.
df.plot(kind='line')
df['Age'].plot(kind='bar')
# Select rows where 'Age' is greater than 30
print(df[df['Age'] > 30])
filtered_df = df[df['Age'] > 30] # Rows where age is greater than 30
print(filtered_df)
Name Age City AgeInTenYears
2 Charlie 35.0 London 45
Name Age City AgeInTenYears
2 Charlie 35.0 London 45
#DATA CLEANING
#Drop Rows With Missing Values
import pandas as pd
# Define a dictionary with Indian employee data containing missing values (None)
data = {
"Employee": ["Aarav", "Priya", "Rahul", "Ananya", "Kabir"],
"Salary_LPA": [12.0, None, 18.5, 9.0, 15.0], # Priya's salary is missing
"City": ["Mumbai", "Delhi", None, "Bangalore", None], # Rahul and Kabir have missing cities
}
df = pd.DataFrame(data)
print("Original Data:\n", df)
print()
# Use dropna() to remove rows that contain any missing values
df_cleaned = df.dropna()
print("Cleaned Data (Rows with any missing values removed):\n", df_cleaned)
Original Data: Employee Salary_LPA City 0 Aarav 12.0 Mumbai 1 Priya NaN Delhi 2 Rahul 18.5 None 3 Ananya 9.0 Bangalore 4 Kabir 15.0 None Cleaned Data (Rows with any missing values removed): Employee Salary_LPA City 0 Aarav 12.0 Mumbai 3 Ananya 9.0 Bangalore
#Fill Missing Values
import pandas as pd
# define a dictionary with sample data which includes some missing values
data = {
'A': [1, 2, 3, None, 5],
'B': [None, 2, 3, 4, 5],
'C': [1, 2, None, None, 5]
}
df = pd.DataFrame(data)
print("Original Data:\n", df)
# filling NaN values with 0
df.fillna(0, inplace=True)
print("\nData after filling NaN with 0:\n", df)
Original Data:
A B C
0 1.0 NaN 1.0
1 2.0 2.0 2.0
2 3.0 3.0 NaN
3 NaN 4.0 NaN
4 5.0 5.0 5.0
Data after filling NaN with 0:
A B C
0 1.0 0.0 1.0
1 2.0 2.0 2.0
2 3.0 3.0 0.0
3 0.0 4.0 0.0
4 5.0 5.0 5.0import pandas as pd
# define a dictionary with sample data which includes some missing values
data = {
'A': [1, 2, 3, None, 5],
'B': [None, 2, 3, 4, 5],
'C': [1, 2, None, None, 5]
}
df = pd.DataFrame(data)
print("Original Data:\n", df)
# filling NaN values with the mean of each column
df.fillna(df.mean(), inplace=True)
print("\nData after filling NaN with mean:\n", df)
Original Data:
A B C
0 1.0 NaN 1.0
1 2.0 2.0 2.0
2 3.0 3.0 NaN
3 NaN 4.0 NaN
4 5.0 5.0 5.0
Data after filling NaN with mean:
A B C
0 1.00 3.5 1.000000
1 2.00 2.0 2.000000
2 3.00 3.0 2.666667
3 2.75 4.0 2.666667
4 5.00 5.0 5.000000
import pandas as pd
# sample data
data = {
'A': [1, 2, 2, 3, 3, 4],
'B': [5, 6, 6, 7, 8, 8]
}
df = pd.DataFrame(data)
print("Original DataFrame:\n", df.to_string(index=False))
# detect duplicates
print("\nDuplicate Rows:\n", df[df.duplicated()].to_string(index=False))
# remove duplicates based on column 'A'
df.drop_duplicates(subset=['A'], keep='first', inplace=True)
print("\nDataFrame after removing duplicates based on column 'A':\n", df.to_string(index=False))
Original DataFrame:
A B
1 5
2 6
2 6
3 7
3 8
4 8
Duplicate Rows:
A B
2 6
DataFrame after removing duplicates based on column 'A':
A B
1 5
2 6
3 7
4 8
