2023年5月6日 20:26:38go评论74阅读模式

英文:

comparing all the rows with in a group pyspark dataframe

问题

Here is the translated code snippet:

我有以下数据帧，我需要在`company`组内比较`first_nm`和`sur_nm`列的行值。根据匹配情况，我会在输出的`status`列中分配一个值。

例如：

如果在特定的`company`组中所有行的`first_nm`和`sur_nm`都匹配 - `status`为0。

如果在`company`组中只有`first_nm`匹配 - `status`为1。

如果在`company`组中只有`sur_nm`匹配 - `status`为2。

如果没有匹配或有空值 - `status`为99。

输出的数据帧如下：

```python
+--------+--------+----------------+--------------+-------+
| company|      id|        first_nm|        sur_nm| status|
+--------+--------+----------------+--------------+-------+
|SYNTHE01|SYNTHE02|           JAMES|        FOWLER|      2|
|SYNTHE01|SYNTHE03|          MONICA|        FOWLER|      2|
|SYNTHE01|SYNTHE04|          GEORGE|        FOWLER|      2|
|SYNTHE08|SYNTHE05|           JAMES|        FIWLER|      1|
|SYNTHE08|SYNTHE06|           JAMES|        FUWLER|      1|
|SYNTHE08|SYNTHE07|           JAMES|        FAWLER|      1|
|SYNTHE08|SYNTHE08|           JAMES|        FEWLER|      1|
|SYNTHE11|SYNTHE12|           JAMES|        FOWLER|      0|
|SYNTHE11|SYNTHE11|           JAMES|        FOWLER|      0|
|SYNTHE09|SYNTHE0X|            Null|          Null|     99|
|SYNTHE09|SYNTHE0Y|            Null|          Null|     99|
|SYNTHE09|SYNTHE0Z|            Null|          Null|     99|
+--------+--------+----------------+--------------+-------+

如何处理不同行值的列内比较，请指导。

谢谢


<details>
<summary>英文:</summary>
I have dataframe as below, where I need to compare the row values of column `first_nm` and `sur_nm` within a group based on `company`. Based on the matching I would assign a value to `status` column in the output.


For eg.
If both `first_nm` and `sur_nm` of all rows matches in a particular `company` - `status` is 0.
If only `first_nm` matches in a `company` group - `status` is 1.
If only `sur_nm` matches in a `company` group - `status` is 2.
If nothing matches or null values - `status` is 99.
The output dataframe is as below:


How can we handle this kind of compare within a column for different row values. Please guide.
Thank you
</details>
# 答案1
**得分**: 2
你的DataFrame（df）：
```plaintext
+--------+--------+--------+------+
| 公司   |   id   | 名字    | 姓氏   |
+--------+--------+--------+------+
|SYNTHE01|SYNTHE02|   JAMES|FOWLER|
|SYNTHE01|SYNTHE03|  MONICA|FOWLER|
|SYNTHE01|SYNTHE04|  GEORGE|FOWLER|
|SYNTHE08|SYNTHE05|   JAMES|FIWLER|
|SYNTHE08|SYNTHE06|   JAMES|FUWLER|
|SYNTHE08|SYNTHE07|   JAMES|FAWLER|
|SYNTHE08|SYNTHE08|   JAMES|FEWLER|
|SYNTHE11|SYNTHE12|   JAMES|FOWLER|
|SYNTHE11|SYNTHE11|   JAMES|FOWLER|
|SYNTHE09|SYNTHE0X|    空  |  空  |
|SYNTHE09|SYNTHE0Y|    空  |  空  |
|SYNTHE09|SYNTHE0Z|    空  |  空  |
+--------+--------+--------+------+

导入必要的包：

from pyspark.sql.functions import col, when, size, collect_set

获取first_nm和sur_nm的唯一计数：

unique_df = df.groupBy("公司").agg(
    size(collect_set("名字")).alias("名字计数"),
    size(collect_set("姓氏")).alias("姓氏计数")
)

应用条件：

company_status_df = unique_df.withColumn("状态",
                   when((col("名字计数") == 1) & (col("姓氏计数") == 1), 0)
                   .when(col("名字计数") == 1, 1)
                   .when(col("姓氏计数") == 1, 2)
                   .otherwise(99)
              ).select("公司", "状态")

与原始DataFrame df 进行连接：

df.join(company_status_df, "公司").show()

输出结果：

+--------+--------+--------+------+------+  
| 公司   |   id   | 名字    | 姓氏   | 状态 |
+--------+--------+--------+------+------+  
|SYNTHE01|SYNTHE02|   JAMES|FOWLER|   2  |
|SYNTHE01|SYNTHE03|  MONICA|FOWLER|   2  |
|SYNTHE01|SYNTHE04|  GEORGE|FOWLER|   2  |
|SYNTHE08|SYNTHE05|   JAMES|FIWLER|   1  |
|SYNTHE08|SYNTHE06|   JAMES|FUWLER|   1  |
|SYNTHE08|SYNTHE07|   JAMES|FAWLER|   1  |
|SYNTHE08|SYNTHE08|   JAMES|FEWLER|   1  |
|SYNTHE11|SYNTHE12|   JAMES|FOWLER|   0  |
|SYNTHE11|SYNTHE11|   JAMES|FOWLER|   0  |
|SYNTHE09|SYNTHE0X|    空  |  空  |  99  |
|SYNTHE09|SYNTHE0Y|    空  |  空  |  99  |
|SYNTHE09|SYNTHE0Z|    空  |  空  |  99  |
+--------+--------+--------+------+------+

请注意，我已将DataFrame中的中文内容翻译成了中文。如果有其他问题，请告诉我。

英文:

Your DataFrame (df):

+--------+--------+--------+------+
| company|      id|first_nm|sur_nm|
+--------+--------+--------+------+
|SYNTHE01|SYNTHE02|   JAMES|FOWLER|
|SYNTHE01|SYNTHE03|  MONICA|FOWLER|
|SYNTHE01|SYNTHE04|  GEORGE|FOWLER|
|SYNTHE08|SYNTHE05|   JAMES|FIWLER|
|SYNTHE08|SYNTHE06|   JAMES|FUWLER|
|SYNTHE08|SYNTHE07|   JAMES|FAWLER|
|SYNTHE08|SYNTHE08|   JAMES|FEWLER|
|SYNTHE11|SYNTHE12|   JAMES|FOWLER|
|SYNTHE11|SYNTHE11|   JAMES|FOWLER|
|SYNTHE09|SYNTHE0X|    null|  null|
|SYNTHE09|SYNTHE0Y|    null|  null|
|SYNTHE09|SYNTHE0Z|    null|  null|
+--------+--------+--------+------+

Importing necessary packages

from pyspark.sql.functions import col, when, size, collect_set

Get the unique count of first_nm and sur_nm

unique_df = df.groupBy(&quot;company&quot;).agg(
size(collect_set(&quot;first_nm&quot;)).alias(&quot;first_nm_size&quot;),
size(collect_set(&quot;sur_nm&quot;)).alias(&quot;sur_nm_size&quot;)
)

Apply the condition

company_status_df = unique_df.withColumn(&quot;status&quot;, 
when((col(&quot;first_nm_size&quot;) == 1) &amp; (col(&quot;sur_nm_size&quot;) == 1), 0)
.when(col(&quot;first_nm_size&quot;) == 1, 1)
.when(col(&quot;sur_nm_size&quot;) == 1, 2)
.otherwise(99)
).select(&quot;company&quot;, &quot;status&quot;)

Join it with the original DataFrame df

df.join(company_status_df, &quot;company&quot;).show()

Output:

+--------+--------+--------+------+------+
| company|      id|first_nm|sur_nm|status|
+--------+--------+--------+------+------+
|SYNTHE01|SYNTHE02|   JAMES|FOWLER|     2|
|SYNTHE01|SYNTHE03|  MONICA|FOWLER|     2|
|SYNTHE01|SYNTHE04|  GEORGE|FOWLER|     2|
|SYNTHE08|SYNTHE05|   JAMES|FIWLER|     1|
|SYNTHE08|SYNTHE06|   JAMES|FUWLER|     1|
|SYNTHE08|SYNTHE07|   JAMES|FAWLER|     1|
|SYNTHE08|SYNTHE08|   JAMES|FEWLER|     1|
|SYNTHE11|SYNTHE12|   JAMES|FOWLER|     0|
|SYNTHE11|SYNTHE11|   JAMES|FOWLER|     0|
|SYNTHE09|SYNTHE0X|    null|  null|    99|
|SYNTHE09|SYNTHE0Y|    null|  null|    99|
|SYNTHE09|SYNTHE0Z|    null|  null|    99|
+--------+--------+--------+------+------+

答案2

得分: 2

通过对分组的company列使用多个when条件（用于在组内计算count distinct值），可以实现如下代码：

import pyspark.sql.functions as F

df = df.join(df.groupby('company')
             .agg(F.when((F.countDistinct('first_nm') == 1) & (F.countDistinct('sur_nm') == 1), 0)
                   .when(F.countDistinct('first_nm') == 1, 1)
                   .when(F.countDistinct('sur_nm') == 1, 2).otherwise(99)
                   .alias('status')), on='company')

df.show(truncate=False)

这段代码将根据不同的条件计算status列的值，并将结果连接回原始的DataFrame。

英文:

Via multiple when conditions (to count distinct values in groups) on grouped company column:

import pyspark.sql.functions as F
df = df.join(df.groupby(&#39;company&#39;)
.agg(F.when((F.countDistinct(&#39;first_nm&#39;) == 1) &amp; (F.countDistinct(&#39;sur_nm&#39;) == 1), 0)
.when(F.countDistinct(&#39;first_nm&#39;) == 1, 1)
.when(F.countDistinct(&#39;sur_nm&#39;) == 1, 2).otherwise(99)
.alias(&#39;status&#39;)), on=&#39;company&#39;)
df.show(truncate=False)

+--------+--------+--------+------+------+
|company |id      |first_nm|sur_nm|status|
+--------+--------+--------+------+------+
|SYNTHE01|SYNTHE02|JAMES   |FOWLER|2     |
|SYNTHE01|SYNTHE03|MONICA  |FOWLER|2     |
|SYNTHE01|SYNTHE04|GEORGE  |FOWLER|2     |
|SYNTHE08|SYNTHE05|JAMES   |FIWLER|1     |
|SYNTHE08|SYNTHE06|JAMES   |FUWLER|1     |
|SYNTHE08|SYNTHE07|JAMES   |FAWLER|1     |
|SYNTHE08|SYNTHE08|JAMES   |FEWLER|1     |
|SYNTHE11|SYNTHE12|JAMES   |FOWLER|0     |
|SYNTHE11|SYNTHE11|JAMES   |FOWLER|0     |
|SYNTHE09|SYNTHE0X|null    |null  |99    |
|SYNTHE09|SYNTHE0Y|null    |null  |99    |
|SYNTHE09|SYNTHE0Z|null    |null  |99    |
+--------+--------+--------+------+------+

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