Shift pyspark column value to left by one - dataframe

Question

I have a pyspark dataframe that looks like this:
|name|age|height |weight
+-------------+--------------------+------------------------+------------------------+-------------------------+--------------------+------------------+------------------+------------+
| |Mike |20|6-7|
As you can see the values and the column names are not aligned. For example, "Mike" should be under the column of "name", instead of age.
How can I shift the values to left by one so it can match the column name?
The ideal dataframe looks like:
|name|age|height |weight
+-------------+--------------------+------------------------+------------------------+-------------------------+--------------------+------------------+------------------+------------+
| Mike |20 |6-0|160|
Please note that the above data is just an example. In reality I have more than 200 columns and more than 1M rows of data.

Answer 1

Try with .toDF with new column names by dropping name column from the dataframe.
Example:
df=spark.createDataFrame([('','Mike',20,'6-7',160)],['name','age','height','weight'])
df.show()
#+----+----+------+------+---+
#|name| age|height|weight| _5|
#+----+----+------+------+---+
#| |Mike| 20| 6-7|160|
#+----+----+------+------+---+
#select all columns except name
df1=df.select(*[i for i in df.columns if i != 'name'])
drop_col=df.columns.pop()
req_cols=[i for i in df.columns if i != drop_col]
df1.toDF(*req_cols).show()
#+----+---+------+------+
#|name|age|height|weight|
#+----+---+------+------+
#|Mike| 20| 6-7| 160|
#+----+---+------+------+
Using spark.createDataFrame():
cols=['name','age','height','weight']
spark.createDataFrame(df.select(*[i for i in df.columns if i != 'name']).rdd,cols).show()
#+----+---+------+------+
#|name|age|height|weight|
#+----+---+------+------+
#|Mike| 20| 6-7| 160|
#+----+---+------+------+
If you are creating dataframe while reading a file then define schema having first column name as dummy then once you read the data drop the column using .drop() function.
spark.read.schema(<struct_type schema>).csv(<path>).drop('<dummy_column_name>')
spark.read.option("header","true").csv(<path>).toDF(<columns_list_with dummy_column>).drop('<dummy_column_name>')