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