Data Structures

Series:

• A one-dimensional array-like object that can hold any data type
• Can be created from a list, NumPy array or dictionary
• Each element in a Series as an index

data = [1, 2, 3, 4]
series = pd.Series(data)

Dataframe

• A two dimensional table of data with rows and columns
• Can be thought of as a collection of Series
• Can be created from a dictionary, NumPy array or another DataFrame

data = {
	"name": ["Alice", "Bob", "Charlie"],
	"age": [12, 23, 34]
}
df = pd.DataFrame(data)

Basic Operations

• Indexing and Slicing
  • Use square brackets [] to acccess columns by name
  • Use .loc[] to do label based indexing and .iloc[] to do integer based indexing
• Use .describe() to get summary statistics of numeric columns
• .mean(), .median() .std() provide individual statistics

df.descibe()
df['age].mean()

Data Cleaning

• Use methods like .dropna() or .fillna(value=0) to handle missing values