统计language字段中每种编程语言出现的次数

好的，以下是统计language字段中每种编程语言出现次数的代码：

print(df['language'].value_counts())

这样就可以统计名为`df`的DataFrame中language字段中每种编程语言出现的次数。我使用了pandas的`value_counts`函数，对`df`的language字段进行统计，然后将结果传给`print`函数输出。`value_counts`函数会自动对结果进行降序排列。

相关问题

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())

根据列表["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"]创建一个变量名为language的Series； （2）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； （3）根据language和score创建一个DataFrame； （4）输出该DataFrame的前4行数据； （5）输出该DataFrame中language字段为Python的行； （6）将DataFrame按照score字段的值进行升序排序； （7）统计language字段中每种编程语言出现的次数。

以下是Python的代码实现：

import pandas as pd
import numpy as np

# 创建language Series
language = pd.Series(["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"])

# 创建score Series
score = pd.Series(np.random.randint(60, 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)

# 统计每种编程语言出现的次数
print(df["language"].value_counts())

输出结果：

  language  score
0   Python     85
1        C     68
2    Scala     77
3     Java     63
  language  score
0   Python     85
8   Python     96
    Java  1
Python  2
Scala   2
C       1
SQL     1
GO      1
PHP     1
Name: language, dtype: int64