blocks|key|1820403|text|要么是printSchema|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1820404|import+datetime

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

root
+%7C--+_1:+string+(nullable+=+true)
+%7C--+_2:+double+(nullable+=+true)
+%7C--+_3:+long+(nullable+=+true)
+%7C--+_4:+boolean+(nullable+=+true)
+%7C--+_5:+timestamp+(nullable+=+true)|code-block|syntax|javascript|1820405|或查看dtypes|1820406|df.dtypes

[('_1',+'string'),
+('_2',+'double'),
+('_3',+'bigint'),
+('_4',+'boolean'),
+('_5',+'timestamp')]|1820407|entityMap^0|3|B|0|0|3|6|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

Either <code>printSchema</code>

<pre><code>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)
</code></pre>

or check <code>dtypes</code>

<pre><code>df.dtypes

[('_1', 'string'),
 ('_2', 'double'),
 ('_3', 'bigint'),
 ('_4', 'boolean'),
 ('_5', 'timestamp')]
</code></pre>

blocks|key|1502858|text|下面的代码应该会得到您想要的结果|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1502859|#+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)+%2B+1


#+query+dict_count+to+find+the+number+of+times+a+data+type+is+present+in+data+frame
dict_count++|code-block|syntax|javascript|1502860|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The below code should get you your desired result

<pre><code># 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 
</code></pre>

blocks|key|1820396|text|我认为最简单的方法是使用collections.Counter|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1820397|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})|code-block|syntax|javascript|1820398|还有pyspark.sql.DataFrame.describe()方法：|1820399|df.describe().show()
%2B-------%2B------------------%2B------------------%2B----------%2B
%7Csummary%7C+++++++++++int_col%7C+++++++++float_col%7Cstring_col%7C
%2B-------%2B------------------%2B------------------%2B----------%2B
%7C++count%7C+++++++++++++++++2%7C+++++++++++++++++3%7C+++++++++3%7C
%7C+++mean%7C+++++++++++++++1.5%7C+++++++++++++++2.3%7C++++++null%7C
%7C+stddev%7C0.7071067811865476%7C1.0999999999999999%7C++++++null%7C
%7C++++min%7C+++++++++++++++++1%7C+++++++++++++++1.2%7C+++++++bar%7C
%7C++++max%7C+++++++++++++++++2%7C+++++++++++++++3.4%7C+++++++foo%7C
%2B-------%2B------------------%2B------------------%2B----------%2B|1820400|请注意，int_col的count为2，因为在本例中其中一个值为null。|1820401|entityMap^0|C|J|0|0|2|W|0|0|4|7|C|5|W|4|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|17|8|@]|D|@]|E|$]]]|R|$]]

I think the easiest way is to use <code>collections.Counter</code>:

<pre class="lang-python prettyprint-override"><code>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})
</code></pre>

And there's also <code>pyspark.sql.DataFrame.describe()</code> method:

<pre class="lang-python prettyprint-override"><code>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|
+-------+------------------+------------------+----------+
</code></pre>

Notice that <code>int_col</code> has a <code>count</code> of 2, since one of the value is <code>null</code> in this example.

How can I view the count of each data type in a Spark Dataframe like I would if I used a pandas dataframe?

For example, assuming df is a pandas dataframe:

<pre><code>&gt;&gt;&gt; df.info(verbose=True)
&lt;class 'pandas.core.frame.DataFrame'&gt;
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
</code></pre>

We can very clearly see the count of each data type. How can I do something similar with a spark dataframe? That is, how can see how many columns are float, how many columns are int, and how many columns are objects?

Thanks!

PySpark - Show a count of column data types in a dataframe

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我如何查看Spark dataframe中每种数据类型的计数，就像我使用pandas数据帧一样？例如，假设df是一个pandas数据帧：>>> df.info(verbose=True)<class 'pandas.core.frame.DataFrame'>RangeIndex: 5 entries, 0 to 4D...

问PySpark -显示数据帧中列数据类型的计数
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