[英]Rename columns with special characters in python or Pyspark dataframe
我在python / pyspark中有一个数据框。 这些列具有特殊字符,例如点(。),空格,括号(())和括号{}。 以他们的名字。
现在,我想以这样的方式重命名列名称:如果有点和空格,请用下划线替换它们;如果有()和{},则将其从列名称中删除。
我已经做到了
df1 = df.toDF(*(re.sub(r'[\.\s]+', '_', c) for c in df.columns))
这样,我就可以用下划线替换点和空格,并且不能执行第二位,即如果()和{}在那里,则从列名称中删除它们。
我们如何实现这一目标。
Python 3.x解决方案:
tran_tab = str.maketrans({x:None for x in list('{()}')})
df1 = df.toDF(*(re.sub(r'[\.\s]+', '_', c).translate(tran_tab) for c in df.columns))
Python 2.x解决方案:
df1 = df.toDF(*(re.sub(r'[\.\s]+', '_', c).translate(None, '(){}') for c in df.columns))
如果您有pyspark数据框,则可以尝试使用withColumnRenamed函数重命名列。 我确实尝试过,看看并为您的更改自定义它。
>>> l=[('some value1','some value2','some value 3'),('some value4','some value5','some value 6')]
>>> l_schema = StructType([StructField("col1.some valwith(in)and{around}",StringType(),True),StructField("col2.some valwith()and{}",StringType(),True),StructField("col3 some()valwith.and{}",StringType(),True)])
>>> reps=('.','_'),(' ','_'),('(',''),(')',''),('{','')('}','')
>>> rdd = sc.parallelize(l)
>>> df = sqlContext.createDataFrame(rdd,l_schema)
>>> df.printSchema()
root
|-- col1.some valwith(in)and{around}: string (nullable = true)
|-- col2.some valwith()and{}: string (nullable = true)
|-- col3 some()valwith.and{}: string (nullable = true)
>>> df.show()
+------------------------+------------------------+------------------------+
|col1.some valwith(in)and{around}|col2.some valwith()and{}|col3 some()valwith.and{}|
+------------------------+------------------------+------------------------+
| some value1| some value2| some value 3|
| some value4| some value5| some value 6|
+------------------------+------------------------+------------------------+
>>> def colrename(x):
... return reduce(lambda a,kv : a.replace(*kv),reps,x)
>>> for i in df.schema.names:
... df = df.withColumnRenamed(i,colrename(i))
>>> df.printSchema()
root
|-- col1_some_valwithinandaround: string (nullable = true)
|-- col2_some_valwithand: string (nullable = true)
|-- col3_somevalwith_and: string (nullable = true)
>>> df.show()
+--------------------+--------------------+--------------------+
|col1_some_valwithinandaround|col2_some_valwithand|col3_somevalwith_and|
+--------------------+--------------------+--------------------+
| some value1| some value2| some value 3|
| some value4| some value5| some value 6|
+--------------------+--------------------+--------------------+
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.