NumPy

1. Efficiency

2. NumPy Arrays

3. Getting Basic Information about an Array

4. np.arange()

5. Similar to Lists

6. Different from Lists

7. Universal Functions

Exercise 1: Multiplying Array Elements

1. Multi-dimensional Arrays

Exercise 2: Retrieving Data from an Array

1. Modifying Parts of an Array

2. Adding a Row Vector to All Rows

3. More Ways to Create Arrays

4. Getting the Number of Rows and Columns in an Array

5. Random Sampling

Exercise 3: Rolling Doubles

1. Using Boolean Arrays to Get New Arrays

2. More with NumPy Arrays

pandas

1. Series

2. Other Ways of Creating Series

3. np.nan

4. Accessing Elements from a Series

Exercise 4: Retrieving Data from a Series

1. Series Alignment

Exercise 5: Using Boolean Series to Get New Series

1. Comparing One Series with Another

2. Element-wise Operations and the apply() Method

3. Series: A More Practical Example

4. DataFrame

5. Creating a DataFrame from a NumPy Array

6. Creating a DataFrame using Existing Series as Rows

7. Creating a DataFrame using Existing Series as Columns

8. Creating a DataFrame from a CSV

9. Python Data Analysis with NumPy and Pandas

10. Exploring a DataFrame

11. Getting Columns

Exercise 6: Exploring a DataFrame

1. Cleaning Data

2. Getting Rows

3. Combining Row and Column Selection

4. Scalar Data: at[] and iat[]

5. Boolean Selection

6. Using a Boolean Series to Filter a DataFrame

Exercise 7: Series and DataFrames

1. Plotting with matplotlib

2. Inline Plots in IPython Notebook

3. Line Plot

4. Bar Plot

5. Annotation

Exercise 8: Plotting a DataFrame

1. Other Kinds of Plots