Data Transformations with the WHIN Dataset

This example is from TDM 102 Project 9 Spring 2024.

These example(s) depend on the database:

/anvil/projects/tdm/data/whin/weather.parquet

Learn more about the dataset here.

2a. Find out how many null records exist in myDF, within each individual column. (Your answer should specify, for each column, how many null records are in that column.)

myDF.isnull().sum()

Column Name	Null Count
station_id	0
latitude	0
longitude	0
name	0
observation_time	0
temperature	4176
temperature_high	4123
temperature_low	4123
humidity	4162
solar_radiation	48104
solar_radiation_high	12828
rain	0
rain_inches_last_hour	0
wind_speed_mph	4032
wind_direction_degrees	95545
wind_gust_speed_mph	0
wind_gust_direction_degrees	94216
pressure	0
soil_temp_1	114043
soil_temp_2	114041
soil_temp_3	114037
soil_temp_4	114036
soil_moist_1	114124
soil_moist_2	114170
soil_moist_3	114174
soil_moist_4	114150

2b. Now count the total number of null values in the entire data frame myDF. (In other words, add up the values from all of the counts in part 2a.)

myDF.isnull().sum().sum()

2c. Drop rows with any null values in myDF. Save the resulting cleaned data set into a new DataFrame called myDF_cleaned.

myDF_cleaned = myDF.dropna()

2d. Just to make sure that you did this properly, check myDF_cleaned carefully: Are there any null values remaining in myDF_cleaned? (There should not be.) How many rows and columns are in myDF_cleaned?

myDF_cleaned.isnull().sum().sum()