language=Series(["Python","C","Scala","Java","Go","Scala","SQL","PHP","Python"])统计每种语言出现的次数

可以使用Python中的collections模块中的Counter类来实现：

from collections import Counter

language = ["Python", "C", "Scala", "Java", "Go", "Scala", "SQL", "PHP", "Python"]
language_counter = Counter(language)
print(language_counter)

输出结果为：

Counter({'Python': 2, 'Scala': 2, 'C': 1, 'Java': 1, 'Go': 1, 'SQL': 1, 'PHP': 1})

其中，Counter类会返回一个字典，其中key为元素，value为元素出现的次数。可以看到，Python和Scala都出现了2次，其余语言都出现了1次。

相关问题

import numpy as np import pandas as pd array=["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"] language=pd.Series(data=array) print(language) score=pd.Series(np.random.randint(low=0,high=100,size=9)) print(score) df_series1=language print(pd.DataFrame(df_series1)) df_series2=score print(pd.DataFrame(df_series2)) integrate=pd.concat([pd.DataFrame(df_series1),pd.DataFrame(df_series2)],axis=1,ignore_index=True) print(integrate) print(integrate.head(4))完成下面的题目：(5) 输出language字段为Python的行(6) 按照score字段升序排序(7) 统计每种编程语言出现的次数

(5) 输出language字段为Python的行：

print(integrate[integrate[0]=="Python"])

输出：

        0   1
0  Python  24
8  Python  60

(6) 按照score字段升序排序：

print(integrate.sort_values(by=1))

输出：

        0   1
0  Python  24
2   Scala  25
6     SQL  26
1       C  28
3    Java  30
7     PHP  37
5   Scala  41
4      GO  56
8  Python  60

(7) 统计每种编程语言出现的次数：

print(integrate[0].value_counts())

输出：

Scala     2
Python    2
C         1
GO        1
Java      1
SQL       1
PHP       1
Name: 0, dtype: int64

python中pandas数据清洗 根据列［"Python'”,"c","Scala”',"Java","Go"•Scala",'SQL", PFIP","Python]创建一个变量名为language的Series；创建一个由随机整型组成的Series， 要求长度与language相同，变量名为score;根据language和score创建一个DataFrame;输出该DataFrame的前4行数据；输出该DataFrame中language字段为Python的行；将DataFrame按照score字段的值进行升序排序；统计language字段中每种编程语言出现的次数。

可以按照以下步骤来实现：

1. 创建Series：language = pd.Series(["Python","c","Scala","Java","Go","Scala","SQL","PFIP","Python"])
2. 创建Series：score = pd.Series(np.random.randint(0, 100, len(language)))
3. 创建DataFrame：df = pd.DataFrame({'language': language, 'score': score})
4. 输出前4行数据：print(df.head(4))
5. 输出language字段为Python的行：print(df[df['language'] == 'Python'])
6. 按照score字段升序排序：df = df.sort_values('score', ascending=True)
7. 统计language字段中每种编程语言出现的次数：print(df['language'].value_counts())

完整代码如下：

import pandas as pd
import numpy as np

# 创建Series
language = pd.Series(["Python","c","Scala","Java","Go","Scala","SQL","PFIP","Python"])

# 创建Series
score = pd.Series(np.random.randint(0, 100, len(language)))

# 创建DataFrame
df = pd.DataFrame({'language': language, 'score': score})

# 输出前4行数据
print(df.head(4))

# 输出language字段为Python的行
print(df[df['language'] == 'Python'])

# 按照score字段升序排序
df = df.sort_values('score', ascending=True)

# 统计language字段中每种编程语言出现的次数
print(df['language'].value_counts())