问题
I have a requirement to change column name of a dataframe df
with respect to other dataframe df_col
using pyspark
df
+----+---+----+----+
|code| id|name|work|
+----+---+----+----+
| ASD|101|John| DEV|
| klj|102| ben|prod|
+----+---+----+----+
df_col
+-----------+-----------+
|col_current|col_updated|
+-----------+-----------+
| id| Row_id|
| name| Name|
| code| Row_code|
| Work| Work_Code|
+-----------+-----------+
if df column matches col_current, df column should replace with col_updated. ex: if df.id matches df.col_current, df.id should replace with Row_id.
expected output
Row_id,Name,Row_code,Work_code
101,John,ASD,DEV
102,ben,klj,prod
Note: I want this process to be dynamic.
回答1:
Just collect the df_col
as dictionary:
df = spark.createDataFrame(
[("ASD", "101" "John", "DEV"), ("klj","102", "ben", "prod")],
("code", "id", "name", "work")
)
df_col = spark.createDataFrame(
[("id", "Row_id"), ("name", "Name"), ("code", "Row_code"), ("Work", "Work_Code")],
("col_current", "col_updated")
)
name_dict = df_col.rdd.collectAsMap()
and use select
with list comprehension:
df.select([df[c].alias(name_dict.get(c, c)) for c in df.columns]).printSchema()
# root
# |-- Row_code: string (nullable = true)
# |-- Row_id: string (nullable = true)
# |-- Name: string (nullable = true)
# |-- work: string (nullable = true)
where name_dict
is standard Python dictionary:
{'Work': 'Work_Code', 'code': 'Row_code', 'id': 'Row_id', 'name': 'Name'}
name_dict.get(c, c)
gets new name, given current name, or current name if no match:
name_dict.get("code", "code")
# 'Row_code'
name_dict.get("work", "work") # Case sensitive
# 'work'
and alias
just renames column (df[col]
) to name returned from name_dict.get
.
来源:https://stackoverflow.com/questions/48187400/how-to-change-column-name-of-a-dataframe-with-respect-to-other-dataframe