Question

如何像使用熊猫数据框那样查看Spark数据框中每种数据类型的计数？

例如，假设df是熊猫数据帧：

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
int_col      5 non-null int64
text_col     5 non-null object
float_col    5 non-null float64
**dtypes: float64(1), int64(1), object(1)**
memory usage: 200.0+ bytes

我们可以非常清楚地看到每种数据类型的计数。如何使用Spark数据框执行类似操作？也就是说，如何看到有多少列是浮动的，有多少列是int的，有多少列是对象的？

谢谢！

Answer 1

下面的代码应该可以为您带来理想的结果

# create data frame 
df = sqlContext.createDataFrame(
[(1,'Y','Y',0,0,0,2,'Y','N','Y','Y'),
 (2,'N','Y',2,1,2,3,'N','Y','Y','N'),
 (3,'Y','N',3,1,0,0,'N','N','N','N'),
 (4,'N','Y',5,0,1,0,'N','N','N','Y'),
 (5,'Y','N',2,2,0,1,'Y','N','N','Y'),
 (6,'Y','Y',0,0,3,6,'Y','N','Y','N'),
 (7,'N','N',1,1,3,4,'N','Y','N','Y'),
 (8,'Y','Y',1,1,2,0,'Y','Y','N','N')
],
('id', 'compatible', 'product', 'ios', 'pc', 'other', 'devices', 'customer', 'subscriber', 'circle', 'smb')
)

# Find data types of data frame
datatypes_List = df.dtypes

# Querying datatypes_List gives you column and its data type as a tuple
datatypes_List
[('id', 'bigint'), ('compatible', 'string'), ('product', 'string'), ('ios', 'bigint'), ('pc', 'bigint'), ('other', 'bigint'), ('devices', 'bigint'), ('customer', 'string'), ('subscriber', 'string'), ('circle', 'string'), ('smb', 'string')]

# create empty dictonary to store output values
dict_count = {}

# Loop statement to count number of times the data type is present in the data frame
for x, y in datatypes_List:
    dict_count[y] = dict_count.get(y, 0) + 1


# query dict_count to find the number of times a data type is present in data frame
dict_count

Answer 2

我认为最简单的方法是使用collections.Counter：

df = spark.createDataFrame(
    [(1, 1.2, 'foo'), (2, 2.3, 'bar'), (None, 3.4, 'baz')],
    ["int_col", "float_col", "string_col"]
)

from collections import Counter
print(Counter((x[1] for x in df.dtypes)))
#Counter({'double': 1, 'bigint': 1, 'string': 1})

还有pyspark.sql.DataFrame.describe()方法：

df.describe().show()
+-------+------------------+------------------+----------+
|summary|           int_col|         float_col|string_col|
+-------+------------------+------------------+----------+
|  count|                 2|                 3|         3|
|   mean|               1.5|               2.3|      null|
| stddev|0.7071067811865476|1.0999999999999999|      null|
|    min|                 1|               1.2|       bar|
|    max|                 2|               3.4|       foo|
+-------+------------------+------------------+----------+

请注意，int_col的{{1}}为2，因为在此示例中，值之一为count。

Answer 3

printSchema

import datetime

df = spark.createDataFrame([("", 1.0, 1, True, datetime.datetime.now())])
df.printSchema()

root
 |-- _1: string (nullable = true)
 |-- _2: double (nullable = true)
 |-- _3: long (nullable = true)
 |-- _4: boolean (nullable = true)
 |-- _5: timestamp (nullable = true)

或检查dtypes

df.dtypes

[('_1', 'string'),
 ('_2', 'double'),
 ('_3', 'bigint'),
 ('_4', 'boolean'),
 ('_5', 'timestamp')]

PySpark-显示数据框中的列数据类型计数

3 个答案: