Pandas is a powerful Python library used for data manipulation and analysis. It provides data structures and functions needed to work with structured data seamlessly.
The two primary data structures in Pandas are DataFrame and Series. A DataFrame is a 2-dimensional labeled data structure, similar to a table in a database, while a Series is a 1-dimensional array-like object.
Pandas provides robust methods to detect, handle, and fill missing data, making it easier to clean and prepare datasets for analysis.
Pandas automatically aligns data in computations, which makes it easier to perform operations on data with different indexes.
With Pandas, you can perform complex data wrangling tasks, including reshaping, merging, and grouping data, to transform raw data into a more usable format.
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)
print(df)
Console Output:
Name Age City 0 Alice 25 New York 1 Bob 30 Los Angeles 2 Charlie 35 Chicago
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)
print(df['Name']) # Selecting a single column
print(df[['Name', 'Age']]) # Selecting multiple columns
Console Output:
0 Alice 1 Bob 2 Charlie Name: Name, dtype: object Name Age 0 Alice 25 1 Bob 30 2 Charlie 35
import pandas as pd
import numpy as np
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, np.nan, 35],
'City': ['New York', 'Los Angeles', np.nan]
}
df = pd.DataFrame(data)
print(df.isnull()) # Check for missing values
df.fillna('Unknown', inplace=True) # Fill missing values
print(df)
Console Output:
Name Age City 0 False False False 1 False True False 2 False False True Name Age City 0 Alice 25 New York 1 Bob Unknown Los Angeles 2 Charlie 35 Unknown
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'Salary': [50000, 60000, 70000]
}
df = pd.DataFrame(data)
df['Age'] += 1 # Increment age by 1
df['Salary'] *= 1.1 # Increase salary by 10%
print(df)
Console Output:
Name Age Salary 0 Alice 26 55000.0 1 Bob 31 66000.0 2 Charlie 36 77000.0
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Department': ['HR', 'Finance', 'HR', 'Finance'],
'Salary': [50000, 60000, 70000, 80000]
}
df = pd.DataFrame(data)
grouped = df.groupby('Department').mean()
print(grouped)
Console Output:
Salary Department Finance 70000.0 HR 60000.0
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