Bjørn Jørgensen created SPARK-38067: ---------------------------------------
Summary: Pandas on spark deletes columns with all None as default. Key: SPARK-38067 URL: https://issues.apache.org/jira/browse/SPARK-38067 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 3.2.1 Reporter: Bjørn Jørgensen With pandas {code:java} data = {'col_1': [3, 2, 1, 0], 'col_2': [None, None, None, None]} test_pd = pd.DataFrame.from_dict(data) test_pd.shape {code} (4, 2) {code:java} test_pd.to_json("testpd.json") test_pd2 = pd.read_json("testpd.json") test_pd2.shape {code} (4, 2) Pandas on spark API does delete the column that has all values Null. {code:java} data = {'col_1': [3, 2, 1, 0], 'col_2': [None, None, None, None]} test_ps = ps.DataFrame.from_dict(data) test_ps.shape {code} (4, 2) {code:java} test_ps.to_json("testps.json") test_ps2 = ps.read_json("testps.json/*") test_ps2.shape {code} (4, 1) We need to change this to make pandas on spark API be more like pandas. I have opened a PR for this. -- This message was sent by Atlassian Jira (v8.20.1#820001) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org