pyspark的dataframe如何过滤几列
时间: 2023-09-27 12:05:50 浏览: 34
您可以使用select()方法选择需要的列,然后使用filter()方法进行过滤,例如:
```python
from pyspark.sql.functions import col
# 假设有一个dataframe df,包含三列:col1, col2, col3
df_filtered = df.select("col1", "col3").filter(col("col2") > 0)
```
这将选择col1和col3列,并过滤掉col2列中小于或等于0的记录。
请注意,您需要首先导入col()函数才能使用它。
相关问题
pyspark dataframe
pyspark dataframe是一种基于Spark的分布式数据处理框架,用于处理大规模数据集。它提供了类似于SQL的查询语言和丰富的数据操作功能,可以进行数据筛选、转换、聚合等操作。
以下是两个pyspark dataframe的例子:
1. 使用isin()对数据进行筛选:
```python
from pyspark.sql import Row
# 创建数据
data = [Row(name='Alice', score=78), Row(name='Bob', score=69), Row(name='Jack', score=100)]
df = spark.createDataFrame(data)
使用isin()筛选数据
df.select(df.score.isin([69, 78])).show()
```
输出:
```
+--+
|(score IN (69,78))|
+--+
| true|
| true|
| false|
+--+
```
2. 读取csv文件并显示前10行数据:
```python
from pyspark.sql import SparkSession
from pyspark.sql.types import *
# 创建SparkSession
spark = SparkSession.builder.appName("csvRDD").getOrCreate()
# 定义schema
schema = StructType([
StructField('State', StringType()),
StructField('Color', StringType()), StructField('Count', IntegerType())
])
# 读取csv文件
df = spark.read.format('csv').option("header", True).schema(schema).load(r'/data/mnm_dataset.csv')
# 显示前10行数据
df.show(10)
```
输出:
```
+--+--+--+
|State|Color|Count|
+--+--+--+
| CA| Brown| 36|
| CA| Brown| 29|
| CA| Brown| 39|
| CA| Brown| 38|
| CA| Brown| 28|
| CA| Brown| 35|
| CA| Brown| 41|
| CA| Brown| 33|
| CA| Brown| 36|
| CA| Brown| 32|
+--+--+--+
```
pyspark dataframe saveAsText
To save a PySpark DataFrame as a text file, you can use the `write` method with the `text` format option. Here's an example:
```
df.write.format("text").save("path/to/save/file")
```
In this example, `df` is your PySpark DataFrame, `"text"` is the format option indicating that you want to save it as a text file, and `"path/to/save/file"` is the path where you want to save the file.
Note that this will create a directory with multiple text files, one for each partition of the DataFrame. If you want to save the entire DataFrame as a single text file, you can use the `coalesce` method to reduce the number of partitions to one before saving:
```
df.coalesce(1).write.format("text").save("path/to/save/file")
```