Key Concepts

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Pandas DataFrame creation

Pandas

Selecting Pandas DataFrame rows using logical operators

Pandas apply() function

Pandas DataFrames adding columns

Pandas DataFrame creation

# Ways of creating a Pandas DataFrame
# Passing in a dictionary:
data = {'name':['Anthony', 'Maria'], 'age':[30, 28]}
df = pd.DataFrame(data)

# Passing in a list of lists:
data = [['Tom', 20], ['Jack', 30], ['Meera', 25]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])

# Reading data from a csv file:
df = pd.read_csv('students.csv')

The fundamental Pandas object is called a DataFrame. It is a 2-dimensional size-mutable, potentially heterogeneous, tabular data structure.

A DataFrame can be created multiple ways. It can be created by passing in a dictionary or a list of lists to the pd.DataFrame() method, or by reading data from a CSV file.

Creating, Loading, and Selecting Data with Pandas
Lesson 1 of 2
  1. 1
    Importing the Pandas Module
    Pandas is a Python module for working with tabular data (i.e., data in a table with rows and columns). Tabular data has a lot of the same functionality as SQL or Excel, but Pandas adds the power of…
    Start
  2. 2
    Create a DataFrame I
    A DataFrame is an object that stores data as rows and columns. You can think of a DataFrame as a spreadsheet or as a SQL table. You can manually create a DataFrame or fill it with data from a CSV, …
    Start
  3. 3
    Create a DataFrame II
    You can also add data using lists. For example, you can pass in a list of lists, where each one represents a row of data. Use the keyword argument columns to pass a list of column names. df2 …
    Start
  4. 4
    Comma Separated Variables (CSV)
    We now know how to create our own DataFrame. However, most of the time, we’ll be working with datasets that already exist. One of the most common formats for big datasets is the CSV. *CSV (com…
    Start
  5. 5
    Loading and Saving CSVs
    When you have data in a CSV, you can load it into a DataFrame in Pandas using .read_csv(): pd.read_csv(‘my-csv-file.csv’) In the example above, the .read_csv() method is called. The CSV file cal…
    Start
  6. 6
    Inspect a DataFrame
    When we load a new DataFrame from a CSV, we want to know what it looks like. If it’s a small DataFrame, you can display it by typing print(df). If it’s a larger DataFrame, it’s helpful to be able…
    Start
  7. 7
    Select Columns
    Now we know how to create and load data. Let’s select parts of those datasets that are interesting or important to our analyses. Suppose you have the DataFrame called customers, which contains the…
    Start
  8. 8
    Selecting Multiple Columns
    When you have a larger DataFrame, you might want to select just a few columns. For instance, let’s return to a DataFrame of orders from ShoeFly.com: |id|first_name|last_name|email|shoe_type|sh…
    Start
  9. 9
    Select Rows
    Let’s revisit our orders from ShoeFly.com: |id|first_name|last_name|email|shoe_type|shoe_material|shoe_color| | — | — | — | — | — | — | — | |54791|Rebecca|Lindsay|RebeccaLindsay57…
    Start
  10. 10
    Selecting Multiple Rows
    You can also select multiple rows from a DataFrame. Here are a few more rows from ShoeFly.com’s orders DataFrame: |id|first_name|last_name|email|shoe_type|shoe_material|shoe_color| |-|-|-|-|-|…
    Start
  11. 11
    Select Rows with Logic I
    You can select a subset of a DataFrame by using logical statements: df[df.MyColumnName == desired_column_value] We have a large DataFrame with information about our customers. A few of the many r…
    Start
  12. 12
    Select Rows with Logic II
    You can also combine multiple logical statements, as long as each statement is in parentheses. For instance, suppose we wanted to select all rows where the customer’s age was under 30 or the cus…
    Start
  13. 13
    Select Rows with Logic III
    Suppose we want to select the rows where the customer’s name is either “Martha Jones”, “Rose Tyler” or “Amy Pond”. |name|address|phone|age| |-|-|-|-| |Martha Jones|123 Main St.|234-567-8910|2…
    Start
  14. 14
    Setting indices
    When we select a subset of a DataFrame using logic, we end up with non-consecutive indices. This is inelegant and makes it hard to use .iloc(). We can fix this using the method .reset_index(). F…
    Start
  15. 15
    Review
    You’ve completed the lesson! You’ve just learned the basics of working with a single table in Pandas, including: - Create a table from scratch - Loading data from another file - Selecting certain …
    Start

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