我有一些通过通道名称存储在表中的传感器数据，而不是传感器名称（这是为了避免由于许多传感器仅在少数设备上使用而具有非常宽的表 - 稀疏的工作列，我知道，但我只是数据的用户）。像这样：
from functools import reduce
import numpy as np
import pandas as pd
np.random.seed(0)
data_df = pd.DataFrame({
'id': ['a']*5 + ['b']*5 + ['c']*5,
'chan1': range(15),
'chan2': np.random.uniform(0, 10, size=15),
'chan3': np.random.uniform(0, 100, size=15)
})
第二个表告诉我们如何根据设备的特定ID将通道名称映射到传感器名称：
sensor_channel_df = pd.DataFrame([
{'id': 'a', 'channel': 'chan1', 'sensor': 'weight'},
{'id': 'a', 'channel': 'chan2', 'sensor': 'torque'},
{'id': 'a', 'channel': 'chan3', 'sensor': 'temp'},
{'id': 'b', 'channel': 'chan1', 'sensor': 'weight'},
{'id': 'b', 'channel': 'chan2', 'sensor': 'temp'},
{'id': 'b', 'channel': 'chan3', 'sensor': 'speed'},
{'id': 'c', 'channel': 'chan1', 'sensor': 'temp'},
{'id': 'c', 'channel': 'chan2', 'sensor': 'weight'},
{'id': 'c', 'channel': 'chan3', 'sensor': 'acceleration'},
])
我可以像这样创建一个重命名字典：
channel_rename_dict = sensor_channel_df.groupby('id')\
.apply(lambda grp: dict(zip(grp['channel'], grp['sensor'])))\
.to_dict()
然后使用groupby / apply：
data_df.groupby('id')\
.apply(lambda group: group.rename(columns=channel_rename_dict[group.name]))\
.reset_index(level=0, drop=True)
我们得到这样的结果：
acceleration id speed temp torque weight
0 NaN a NaN 8.712930 5.488135 0.000000
1 NaN a NaN 2.021840 7.151894 1.000000
2 NaN a NaN 83.261985 6.027634 2.000000
3 NaN a NaN 77.815675 5.448832 3.000000
4 NaN a NaN 87.001215 4.236548 4.000000
5 NaN b 97.861834 6.458941 NaN 5.000000
6 NaN b 79.915856 4.375872 NaN 6.000000
7 NaN b 46.147936 8.917730 NaN 7.000000
8 NaN b 78.052918 9.636628 NaN 8.000000
9 NaN b 11.827443 3.834415 NaN 9.000000
10 63.992102 c NaN 10.000000 NaN 7.917250
11 14.335329 c NaN 11.000000 NaN 5.288949
12 94.466892 c NaN 12.000000 NaN 5.680446
13 52.184832 c NaN 13.000000 NaN 9.255966
14 41.466194 c NaN 14.000000 NaN 0.710361
这一切都很好（虽然我不会惊讶地发现在熊猫中有更好的方法），并且我用它来向一些同事展示这个过程的逻辑。
然而，对于项目架构，我们决定使用spark。有没有办法在Spark数据帧中实现相同的行为？
我最初的想法是首先cache完整data_df，然后使用id分析filter上的数据框。例如，假设data_df现在是火花数据帧：
data_df.cache()
unique_ids = data_df.select('id').distinct().rdd.map(lambda row: row[0]).collect()
split_dfs = {id: data_df.filter(data_df['id'] == id) for id in unique_ids}
然后，如果我们像以前一样拥有列重命名字典，我们可以执行以下操作：
dfs_paired_with_rename_tuple_lists = [
(split_dfs[id], list(channel_rename_dict[id].items()))
for id in unique_ids
]
new_dfs = [
reduce(lambda df_i, rename_tuple: df_i.withColumnRenamed(*rename_tuple), rename_tuple_list, df)
for df, rename_tuple_list in dfs_paired_with_rename_tuple_lists
]
然后，我可以在确保它们具有公共列后，在此火花数据帧列表上执行reduce Union()。
我的感觉是，这会非常缓慢，并且可能有更好的方法来解决这个问题。
答案 0 :(得分：1)
首先，让我们按channel重新定义映射到分组并返回MapType Column（toolz很方便，但可以替换为itertools.chain）*：
from toolz import concat, interleave
from pyspark.sql.functions import col, create_map, lit, struct
# Create literal column from id to sensor -> channel map
channel_map = create_map(*concat((lit(k), v) for k, v in sensor_channel_df
.groupby("id")
# Create map Column from literal label to channel
.apply(lambda grp: create_map(*interleave([
map(lit, grp["sensor"]),
map(col, grp["channel"])])))
.to_dict()
.items()))
接下来，获取传感器列表：
sensors = sorted(sensor_channel_df["sensor"].unique().tolist())
并组合数据列：
df = spark.createDataFrame(data_df)
data_cols = struct(*[c for c in df.columns if c != "id"])
上面定义的组件可以合并：
cols = [channel_map[col("id")][sensor].alias(sensor) for sensor in sensors]
df.select(["id"] + cols)
+---+------------------+------------------+------------------+------------------+------------------+
| id| acceleration| speed| temp| torque| weight|
+---+------------------+------------------+------------------+------------------+------------------+
| a| null| null| 8.712929970154072|5.4881350392732475| 0.0|
| a| null| null| 2.021839744032572| 7.151893663724195| 1.0|
| a| null| null| 83.2619845547938| 6.027633760716439| 2.0|
| a| null| null| 77.81567509498505| 5.448831829968968| 3.0|
| a| null| null| 87.00121482468191| 4.236547993389047| 4.0|
| b| null| 97.8618342232764| 6.458941130666561| null| 5.0|
| b| null| 79.91585642167236| 4.375872112626925| null| 6.0|
| b| null|46.147936225293186| 8.917730007820797| null| 7.0|
| b| null| 78.05291762864555| 9.636627605010293| null| 8.0|
| b| null|11.827442586893323|3.8344151882577773| null| 9.0|
| c| 63.99210213275238| null| 10.0| null| 7.917250380826646|
| c| 14.33532874090464| null| 11.0| null| 5.288949197529044|
| c| 94.46689170495839| null| 12.0| null| 5.680445610939323|
| c|52.184832175007166| null| 13.0| null| 9.25596638292661|
| c| 41.46619399905236| null| 14.0| null|0.7103605819788694|
+---+------------------+------------------+------------------+------------------+------------------+
使用udf：
from toolz import unique
from pyspark.sql.types import *
from pyspark.sql.functions import udf
channel_dict = (sensor_channel_df
.groupby("id")
.apply(lambda grp: dict(zip(grp["sensor"], grp["channel"])))
.to_dict())
def remap(d):
fields = sorted(unique(concat(_.keys() for _ in d.values())))
schema = StructType([StructField(f, DoubleType()) for f in fields])
def _(row, id):
return tuple(float(row[d[id].get(f)]) if d[id].get(f) is not None
else None for f in fields)
return udf(_, schema)
(df
.withColumn("vals", remap(channel_dict)(data_cols, "id"))
.select("id", "vals.*"))
+---+------------------+------------------+------------------+------------------+------------------+
| id| acceleration| speed| temp| torque| weight|
+---+------------------+------------------+------------------+------------------+------------------+
| a| null| null| 8.712929970154072|5.4881350392732475| 0.0|
| a| null| null| 2.021839744032572| 7.151893663724195| 1.0|
| a| null| null| 83.2619845547938| 6.027633760716439| 2.0|
| a| null| null| 77.81567509498505| 5.448831829968968| 3.0|
| a| null| null| 87.00121482468191| 4.236547993389047| 4.0|
| b| null| 97.8618342232764| 6.458941130666561| null| 5.0|
| b| null| 79.91585642167236| 4.375872112626925| null| 6.0|
| b| null|46.147936225293186| 8.917730007820797| null| 7.0|
| b| null| 78.05291762864555| 9.636627605010293| null| 8.0|
| b| null|11.827442586893323|3.8344151882577773| null| 9.0|
| c| 63.99210213275238| null| 10.0| null| 7.917250380826646|
| c| 14.33532874090464| null| 11.0| null| 5.288949197529044|
| c| 94.46689170495839| null| 12.0| null| 5.680445610939323|
| c|52.184832175007166| null| 13.0| null| 9.25596638292661|
| c| 41.46619399905236| null| 14.0| null|0.7103605819788694|
+---+------------------+------------------+------------------+------------------+------------------+
在Spark 2.3或更高版本中，您可以将当前代码应用于vectorized UDF。
*为了理解这里发生了什么，让我们看一个由apply处理的单个组：
grp = sensor_channel_df.groupby("id").get_group("a")
首先，我们将sensor传感器列转换为一系列Spark文字Columns（考虑常量值）：
keys = list(map(lit, grp["sensor"]))
keys
Column<b'weight'>, Column<b'torque'>, Column<b'temp'>]
和sensor列到Spark Columns的序列（想想指向数据的指针）：
values = list(map(col, grp["channel"]))
values
[Column<b'chan1'>, Column<b'chan2'>, Column<b'chan3'>]
在上下文中进行评估时，前者将导致输出不变：
df_ = df.drop_duplicates(subset=["id"])
df_.select(keys).show()
+------+------+----+
|weight|torque|temp|
+------+------+----+
|weight|torque|temp|
|weight|torque|temp|
|weight|torque|temp|
+------+------+----+
而后者将重复数据：
df_.select(values).show(3)
+-----+------------------+-----------------+
|chan1| chan2| chan3|
+-----+------------------+-----------------+
| 10| 7.917250380826646|63.99210213275238|
| 5| 6.458941130666561| 97.8618342232764|
| 0|5.4881350392732475|8.712929970154072|
+-----+------------------+-----------------+
接下来，我们将这两个交错并合并为一个MapType列：
mapping = create_map(*interleave([keys, values]))
mapping
Column<b'map(weight, chan1, torque, chan2, temp, chan3)'>
这使我们能够将度量标准名称映射到数据列（考虑Python dict），并在评估时使用：
df_.select(mapping).show(3, False)
+---------------------------------------------------------------------------+
|map(weight, chan1, torque, chan2, temp, chan3) |
+---------------------------------------------------------------------------+
|Map(weight -> 10.0, torque -> 7.917250380826646, temp -> 63.99210213275238)|
|Map(weight -> 5.0, torque -> 6.458941130666561, temp -> 97.8618342232764) |
|Map(weight -> 0.0, torque -> 5.4881350392732475, temp -> 8.712929970154072)|
+---------------------------------------------------------------------------+
最后，外部理解会为所有群体重复此操作，因此channel_map是Column：
Column<b'map(a, map(weight, chan1, torque, chan2, temp, chan3), b, map(weight, chan1, temp, chan2, speed, chan3), c, map(temp, chan1, weight, chan2, acceleration, chan3))'>
给出了以下结构：
df_.select(channel_map.alias("channel_map")).show(3, False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|Map(a -> Map(weight -> 10.0, torque -> 7.917250380826646, temp -> 63.99210213275238), b -> Map(weight -> 10.0, temp -> 7.917250380826646, speed -> 63.99210213275238), c -> Map(temp -> 10.0, weight -> 7.917250380826646, acceleration -> 63.99210213275238))|
|Map(a -> Map(weight -> 5.0, torque -> 6.458941130666561, temp -> 97.8618342232764), b -> Map(weight -> 5.0, temp -> 6.458941130666561, speed -> 97.8618342232764), c -> Map(temp -> 5.0, weight -> 6.458941130666561, acceleration -> 97.8618342232764)) |
|Map(a -> Map(weight -> 0.0, torque -> 5.4881350392732475, temp -> 8.712929970154072), b -> Map(weight -> 0.0, temp -> 5.4881350392732475, speed -> 8.712929970154072), c -> Map(temp -> 0.0, weight -> 5.4881350392732475, acceleration -> 8.712929970154072))|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
最后，我们使用id列来选择感兴趣的map：
df_.select(channel_map[col("id")].alias("data_mapping")).show(3, False)
+---------------------------------------------------------------------------------+
|data_mapping |
+---------------------------------------------------------------------------------+
|Map(temp -> 10.0, weight -> 7.917250380826646, acceleration -> 63.99210213275238)|
|Map(weight -> 5.0, temp -> 6.458941130666561, speed -> 97.8618342232764) |
|Map(weight -> 0.0, torque -> 5.4881350392732475, temp -> 8.712929970154072) |
+---------------------------------------------------------------------------------+
和列名称，用于从map：
df_.select(channel_map[col("id")]["weight"].alias("weight")).show(3, False)
+-----------------+
|weight |
+-----------------+
|7.917250380826646|
|5.0 |
|0.0 |
+-----------------+
在一天结束时，这只是对包含符号表达式的数据结构的一系列简单转换。