PySpark na.fill not replacing null values with 0 in DF - pyspark

Question

I am using the following code sample:
paths = ["/FileStore/tables/data.csv"]
infer_schema = "true"
df= sqlContext.read \
.format("com.databricks.spark.csv") \
.option("inferSchema", infer_schema) \
.option("header", "true") \
.load(paths)
df.printSchema()
root |-- key: string (nullable = true) |-- dt:
string (nullable = true) |-- key1: string (nullable =
true) |-- key2: string (nullable = true) |-- sls: string
(nullable = true) |-- uts: string (nullable = true) |-- key3:
string (nullable = true)
I did the following to count the null values for the fields sls and uts
df.select([count(when(col(c).isNull(), c)).alias(c) for c in df.columns]).show()
+-------------+--------+------------------+-----------+-----+-----+---------+
|key| dt| key1| key2| sls| uts| key3|
+-------------+--------+------------------+-----------+-----+-----+---------+
| 0| 0| 0| 0| 616| 593| 0|
+-------------+--------+------------------+-----------+-----+-----+---------+
I did the following first:
df.na.fill({'sls': 0, 'uts': 0})
Then I realized these are string fields. So, I did:
df.na.fill({'sls': '0', 'uts': '0'})
After doing this, if I do :
df.filter("sls is NULL").show()
I see null values for sls field:
key| dt| key1| key2| sls| uts| key3|
+-------------+----------+------------------+-----------+-----+-----+-----------+
| -1| 7/13/2020| 8000|41342299215| null| 1|1.70228E+25|
| -1| 12/5/2019| 8734| 8983349833| null| 1|1.76412E+26|
| -1| 1/7/2020| 8822| 1E+15| null| 1|4.69408E+24|
| -1| 12/5/2018| 6768| 1E+15| null| 1|4.54778E+24|
It's the same thing if I do:
df.filter("uts is NULL").show()
Is there something I am missing? Why am I unable to replace the null values with 0?

Answer 1

.na.fill returns a new data frame with null values being replaced. You just need to assign the result to df variable in order for the replacement to take effect:
df = df.na.fill({'sls': '0', 'uts': '0'})