根据列表["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"]创建一个变量名为language的Series
时间: 2023-07-03 20:33:52 浏览: 610
好的,以下是创建Series的代码:
```python
import pandas as pd
lst = ["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"]
language = pd.Series(lst)
```
这样就成功创建了一个变量名为`language`的Series,其中包含了列表中的所有元素。
相关问题
(1)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。
```python
import pandas as pd
import numpy as np
# 创建Series
language = pd.Series(["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"])
# 创建随机整型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)
# 统计每种编程语言出现的次数
count = df['language'].value_counts()
print(count)
```
输出结果:
```
language score
0 Python 64
1 C 96
2 Scala 79
3 Java 72
language score
0 Python 64
8 Python 67
Scala 2
Python 2
C 1
SQL 1
Java 1
PHP 1
GO 1
Name: language, 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:
```python
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())
```
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