Python 从数据框中获取值
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get value out of dataframe
提问by M.Rez
In Scala I can do get(#)or getAs[Type](#)to get values out of a dataframe. How should I do it in pyspark?
在 Scala 中，我可以做get(#)或getAs[Type](#)从数据框中获取值。我应该怎么做pyspark？
I have a two columns DataFrame: item(string)and salesNum(integers). I do a groupbyand meanto get a mean of those numbers like this:
我有两列 DataFrame:item(string)和salesNum(integers). 我做了一个groupby并mean得到这些数字的平均值，如下所示：
saleDF.groupBy("salesNum").mean()).collect()
saleDF.groupBy("salesNum").mean()).collect()
and it works. Now I have the mean in a dataframe with one value.
它有效。现在我在一个数据框中有一个值的平均值。
How can I get that value out of the dataframe to get the mean as a float number?
如何从数据框中获取该值以将平均值作为浮点数？
回答by David
collect()returns your results as a python list. To get the value out of the list you just need to take the first element like this:
collect()将您的结果作为 python 列表返回。要从列表中获取值，您只需要像这样获取第一个元素：
saleDF.groupBy("salesNum").mean()).collect()[0]
回答by Francesco Boi
To be precise, collectreturns a list whose elements are of type class 'pyspark.sql.types.Row'.
准确地说，collect返回一个元素类型为 的列表class 'pyspark.sql.types.Row'。
In your case to extract the real value you should do:
在您提取实际价值的情况下，您应该执行以下操作：
saleDF.groupBy("salesNum").mean()).collect()[0]["avg(yourColumnName)"]
where yourColumnNameis the name of the column you are taking the mean of (pyspark, when applying mean, renames the resulting column in this way by default).
其中yourColumnName是您要取平均值的列的名称（pyspark，在应用平均值时，默认情况下以这种方式重命名结果列）。
As an example, I ran the following code. Look at the types and outputs of each step.
例如，我运行了以下代码。查看每个步骤的类型和输出。
>>> columns = ['id', 'dogs', 'cats', 'nation']
>>> vals = [
... (2, 0, 1, 'italy'),
... (1, 2, 0, 'italy'),
... (3, 4, 0, 'france')
... ]
>>> df = sqlContext.createDataFrame(vals, columns)
>>> df.groupBy("nation").mean("dogs").collect()
[Row(nation=u'france', avg(dogs)=4.0), Row(nation=u'italy', avg(dogs)=1.0)]
>>> df.groupBy("nation").mean("dogs").collect()[0]
Row(nation=u'france', avg(dogs)=4.0))
>>> df.groupBy("nation").mean("dogs").collect()[0]["avg(dogs)"]
4.0
>>> type(df.groupBy("nation").mean("dogs").collect())
<type 'list'>
>>> type(df.groupBy("nation").mean("dogs").collect()[0])
<class 'pyspark.sql.types.Row'>
>>> type(df.groupBy("nation").mean("dogs").collect()[0]["avg(dogs)"])
<type 'float'>
>>>
>>>
