get datatype of column using pyspark

We are reading data from MongoDB Collection. Collection column has two different values (e.g.: (bson.Int64,int) (int,float) ).

I am trying to get a datatype using pyspark.

My problem is some columns have different datatype.

Assume quantity and weight are the columns

quantity           weight
---------          --------
12300              656
123566000000       789.6767
1238               56.22
345                23
345566677777789    21

Actually we didn't defined data type for any column of mongo collection.

When I query to the count from pyspark dataframe

dataframe.count()

I got exception like this

"Cannot cast STRING into a DoubleType (value: BsonString{value='200.0'})"

Your question is broad, thus my answer will also be broad.

To get the data types of your DataFrame columns, you can use dtypes i.e :

>>> df.dtypes
[('age', 'int'), ('name', 'string')]

This means your column age is of type int and name is of type string.

For anyone else who came here looking for an answer to the exact question in the post title (i.e. the data type of a single column, not multiple columns), I have been unable to find a simple way to do so.

Luckily it's trivial to get the type using dtypes:

def get_dtype(df,colname):
    return [dtype for name, dtype in df.dtypes if name == colname][0]

get_dtype(my_df,'column_name')

(note that this will only return the first column's type if there are multiple columns with the same name)

import pandas as pd
pd.set_option('max_colwidth', -1) # to prevent truncating of columns in jupyter

def count_column_types(spark_df):
    """Count number of columns per type"""
    return pd.DataFrame(spark_df.dtypes).groupby(1, as_index=False)[0].agg({'count':'count', 'names': lambda x: " | ".join(set(x))}).rename(columns={1:"type"})

Example output in jupyter notebook for a spark dataframe with 4 columns:

count_column_types(my_spark_df)

I don't know how are you reading from mongodb, but if you are using the mongodb connector, the datatypes will be automatically converted to spark types. To get the spark sql types, just use schema atribute like this:

df.schema

Looks like your actual data and your metadata have different types. The actual data is of type string while the metadata is double.

As a solution I would recommend you to recreate the table with the correct datatypes.