Lecture 7
pandas Basics - Getting a Summary of Data; Selecting Variables; Counting Methods; Sorting Methods
February 12, 2025
nba DataFrame
- Let’s read the nba.csv file as
nba:
# Below is to import the pandas library as pd
import pandas as pd
# Below is for an interactive display of DataFrame in Colab
from google.colab import data_table
data_table.enable_dataframe_formatter()
# Below is to read nba.csv as nba DataFrame
nba = pd.read_csv("https://bcdanl.github.io/data/nba.csv",
parse_dates = ["Birthday"])Getting a Summary of Data
DataFrame Terminologies: Variables, Observations, and Values
- Each variable is a column.
- Each observation is a row.
- Each value is a cell.
DataFrame Terminologies
Dot Operators, Methods, and Attributes
Dot operator
- The dot operator (
DataFrame.) is used for an attribute or a method on objects.
Method
- A method (
DataFrame.METHOD()) is a function that we can call on aDataFrameto perform operations, modify data, or derive insights.- e.g.,
nba.info()
- e.g.,
Attribute
- An attribute (
DataFrame.ATTRIBUTE) is a property that provides information about theDataFrame’s structure or content without modifying it.- e.g.,
nba.dtype
- e.g.,
Getting a Summary of a DataFrame with .info()
- Every
DataFrameobject has a.info()method that provides a summary of a DataFrame:- Variable names (
.columns) - Number of variables/observations (
.shape) - Data type of each variable (
.dtypes) - Number of non-missing values in each variable (
.count())- Pandas often displays missing values as
NaN.
- Pandas often displays missing values as
- Variable names (
Getting a Summary of a DataFrame with .describe()
.describe()method generates descriptive statistics that summarize the central tendency, dispersion, and distribution of each variable.- It can also process
string-type variables if specified explicitly (include='all').
- It can also process
Selecting Variables
Selecting a Variable by its Name
nba_player_name_s = nba['Name'] # Series
nba_player_name_s
nba_player_name_df = nba[ ['Name'] ] # DataFrame
nba_player_name_df- If we want only a specific variable from a
DataFrame, we can access the variable with its name using squared brackets,[ ].DataFrame[ 'var_1' ]DataFrame[ ['var_1'] ]
Selecting Multiple Variables by their Names
- In order to specify multiple variables by their names, we need to pass in a Python list between the square brackets.
DataFrame[ ['var_1', 'var_2', ... ] ]- This is also how we can relocate variables by the order specified in the list.
Selecting Multiple Variables with select_dtypes()
# To include only string variables
nba.select_dtypes(include = "object")
# To exclude string and integer variables
nba.select_dtypes(exclude = ["object", "int"])- We can use the
select_dtypes()method to select columns based on their data types.- The method accepts two parameters,
includeandexclude.
- The method accepts two parameters,
Counting Methods
Counting with .count()
- The
.count()counts the number of non-missing values in aSeries/DataFrame.
Counting with .value_counts()
- The
.value_counts()counts the number of occurrences of each unique value in aSeries/DataFrame.
Counting with .nunique()
- The
.nunique()counts the number of unique values in each variable in aDataFrame.
Pandas Basics
Let’s do Questions 1-3 in Classwork 5!
Lecture 7 pandas Basics - Getting a Summary of Data; Selecting Variables; Counting Methods; Sorting Methods Byeong-Hak Choe bchoe@geneseo.edu SUNY Geneseo February 12, 2025