python - 如何在 Spark Dataframe 中按组/分区重命名列？

Question

我有一些传感器数据按 channel 名称而不是传感器名称存储在表中(这是为了避免由于许多传感器仅在少数设备上使用而导致表格过宽 - 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)
})

还有第二个表告诉我们如何根据设备的特定 ID 将 channel 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()

然后用进一步的groupby/apply重命名所有列:

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(尽管得知在 pandas 中有更好的方法我不会感到惊讶)，我用它向一些同事演示了这个过程的逻辑。

但是，对于项目架构，我们决定使用 spark。有没有一种方法可以在 Spark 数据帧中实现同样的行为？

我最初的想法是首先缓存完整的data_df，然后用filter分解id上的dataframe >。例如，假设 data_df 现在是一个 spark 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
]

在确保它们具有公共(public)列之后，我可以在这个 spark Dataframes 列表上使用 Union() 执行 reduce。

我的感觉是这会非常慢，而且可能有更好的方法来解决这个问题。

Answer 1

首先，让我们将映射重新定义为按channel 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())

并合并数据列:

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组合:

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|
+---+------------------+------------------+------------------+------------------+------------------+

也可以使用 udf，尽管效率较低:

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，您可以使用 vectorized UDF 应用当前代码。

---

* 为了理解这里发生了什么，让我们看一下由 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 序列(考虑指向数据的指针):

values = list(map(col, grp["channel"]))
values

[Column<b'chan1'>, Column<b'chan2'>, Column<b'chan3'>]

当在上下文中评估时，前一个将导致恒定输出:

df_ = df.drop_duplicates(subset=["id"])

df_.select(keys).show()

+------+------+----+
|weight|torque|temp|
+------+------+----+
|weight|torque|temp|
|weight|torque|temp|
|weight|torque|temp|
+------+------+----+

而后者会重复数据:

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组合成一个 MapType 列:

mapping = create_map(*interleave([keys, values]))
mapping 

Column<b'map(weight, chan1, torque, chan2, temp, chan3)'>

这为我们提供了从度量名称到数据列的映射(想想 Python dict)，并且在计算时:

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)|
+---------------------------------------------------------------------------+

最后外部理解对所有组重复此操作，因此 channel_map 是一个 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))'>

评估给出了以下结构:

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)      |
+---------------------------------------------------------------------------------+

和列名以从 map 中提取值:

df_.select(channel_map[col("id")]["weight"].alias("weight")).show(3, False)

+-----------------+
|weight           |
+-----------------+
|7.917250380826646|
|5.0              |
|0.0              |
+-----------------+

归根结底，这只是对包含符号表达式的数据结构进行的一系列简单转换。