Introduction to PySpark DataFrames

Introduction to PySpark

Benjamin Schmidt

Data Engineer

About DataFrames

DataFrames: Tabular format (rows/columns)
Supports SQL-like operations
Comparable to a Pandas Dataframe or a SQL TABLE
Structured Data

Dataframes

Creating DataFrames from filestores

# Create a DataFrame from CSV
census_df = spark.read.csv('path/to/census.csv', header=True, inferSchema=True)

Printing the DataFrame

# Show the first 5 rows of the DataFrame
census_df.show()

   age  education.num marital.status         occupation income
0   90              9        Widowed                  ?  <=50K
1   82              9        Widowed    Exec-managerial  <=50K
2   66             10        Widowed                  ?  <=50K
3   54              4       Divorced  Machine-op-inspct  <=50K
4   41             10      Separated     Prof-specialty  <=50K

Printing DataFrame Schema

# Show the schema
census_df.printSchema()

Output:
root
 |-- age: integer (nullable = true)
 |-- education.num: integer (nullable = true)
 |-- marital.status: string (nullable = true)
 |-- occupation: string (nullable = true)
 |-- income: string (nullable = true)

Basic analytics on PySpark DataFrames

# .count() will return the total row numbers in the DataFrame
row_count = census_df.count()
print(f'Number of rows: {row_count}')

# groupby() allows the use of sql-like aggregations
census_df.groupBy('gender').agg({'salary_usd': 'avg'}).show()

Other aggregate functions are:

sum()
min()
max()

Key functions for PySpark analytics

.select(): Selects specific columns from the DataFrame
.filter(): Filters rows based on specific conditions
.groupBy(): Groups rows based on one or more columns
.agg(): Applies aggregate functions to grouped data

Key Functions For Example

# Using filter and select, we can narrow down our DataFrame
filtered_census_df = census_df.filter(df['age'] > 50).select('age', 'occupation')
filtered_census_df.show()

Output
+---+------------------+
|age|       occupation |
+---+------------------+
| 90|                 ?|
| 82|   Exec-managerial|
| 66|                 ?|
| 54| Machine-op-inspct|
+---+------------------+

Let's practice!

Introduction to PySpark