PySpark toPandas function is changing column type

user_12501318 0
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I have a pyspark dataframe with following schema:

root
 |-- src_ip: integer (nullable = true)
 |-- dst_ip: integer (nullable = true)

When converting this dataframe to pandas via toPandas(), the column type changes from integer in spark to float in pandas:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9847 entries, 0 to 9846
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   src_ip  9607 non-null   float64
 1   dst_ip  9789 non-null   float64
dtypes: float64(2)
memory usage: 154.0 KB

Is there any way to keep integer value with toPandas() or I can only cast column type in resulting pandas dataframe?

pyspark pandas apache-spark python
2 Answers

SPARK-21766 (https://issues.apache.org/jira/browse/SPARK-21766) explains the behavior your observed.

As a workaround, you can call fillna(0) before toPandas():

df1 = sc.createDataFrame([(0, None), (None, 8)], ["src_ip", "dest_ip"])
print(df1.dtypes)

# Reproduce the issue
pdf1 = df1.toPandas()
print(pdf1.dtypes)

# A workaround
pdf2 = df1.fillna(0).toPandas()
print(pdf2.dtypes)
user_132545900

You should use the nullable integer dtype of Pandas

df = spark.createDataFrame([(0, 1), (0, None)], ["a", "b"])
print(df.dtypes)
  
# Cast the integer column to 'Int64'
pdf = df.toPandas()
pdf['b'] = pdf['b'].astype('Int64')
print(pdf.dtypes)
print(pdf)

The capital 'I' in 'Int64' is to differentiate from the NumPy’s 'int64' dtype.

user_82884970
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