[英]How to pivot columns so they turn into rows using PySpark or pandas?
[英]How to Pivot multiple columns in pyspark similar to pandas
我想在 pyspark 中执行类似的操作,就像 pandas 一样
我的 dataframe 是:
Year win_loss_date Deal L2 GFCID Name L2 GFCID GFCID GFCID Name Client Priority Location Deal Location Revenue Deal Conclusion New/Rebid
0 2021 2021-03-08 00:00:00 1-2JZONGU TEST GFCID CREATION P-1-P1DO P-1-P5O TEST GFCID CREATION None UNITED STATES UNITED STATES 4567.0000000 Won New
在此处输入图像描述在 pandas 中: pivot 的代码为:
df = pd.pivot_table(deal_df_pandas,
index=['GFCID', 'GFCID Name', 'Client Priority'],
columns=['New/Rebid', 'Year', 'Deal Conclusion'],
aggfunc={'Deal':'count',
'Revenue':'sum',
'Location': lambda x: set(x),
'Deal Location': lambda x: set(x)}).reset_index()
columns=['New/Rebid', 'Year', 'Deal结论'] ---这些是旋转的列
Output 我得到并期望:
GFCID GFCID Name Client Priority Deal Revenue
New/Rebid New Rebid New Rebid
Year 2020 2021 2020 2021 2020 2021 2020 2021
Deal Conclusion Lost Won Lost Won Lost Won Lost Won Lost Won Lost Won Lost Won Lost Won
0 0000000752 ARAMARK SERVICES INC Bronze NaN 1.0 1.0 2.0 NaN NaN NaN NaN NaN 1600000.0000000 20.0000000 20000.0000000 NaN NaN NaN NaN
在此处输入图像描述我想要的是将上述代码转换为 pyspark。 我正在尝试的不起作用:
from pyspark.sql import functions as F
df_pivot2=(df_d1
.groupby('GFCID', 'GFCID Name', 'Client Priority')
.pivot('New/Rebid').agg(F.first('Year'),F.first('Deal Conclusion'),F.count('Deal'),F.sum('Revenue'))
由于 PySPARK 中无法执行此操作:
(df_d1
.groupby('GFCID', 'GFCID Name', 'Client Priority')
.pivot('New/Rebid','Year','Deal Conclusion') #--error
您可以将多列连接成一列,可以在pivot
中使用。
考虑下面的例子
data_sdf.show()
# +---+-----+--------+--------+
# | id|state| time|expected|
# +---+-----+--------+--------+
# | 1| A|20220722| 1|
# | 1| A|20220723| 1|
# | 1| B|20220724| 2|
# | 2| B|20220722| 1|
# | 2| C|20220723| 2|
# | 2| B|20220724| 3|
# +---+-----+--------+--------+
data_sdf. \
withColumn('pivot_col', func.concat_ws('_', 'state', 'time')). \
groupBy('id'). \
pivot('pivot_col'). \
agg(func.sum('expected')). \
fillna(0). \
show()
# +---+----------+----------+----------+----------+----------+
# | id|A_20220722|A_20220723|B_20220722|B_20220724|C_20220723|
# +---+----------+----------+----------+----------+----------+
# | 1| 1| 1| 0| 2| 0|
# | 2| 0| 0| 1| 3| 2|
# +---+----------+----------+----------+----------+----------+
输入 dataframe 有 2 个字段 - state
和time
- 将被旋转。 它们与'_'
分隔符连接并在pivot
中使用。 您可以根据您的要求在agg
中使用多个聚合,然后发布。
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.