统计language字段中每种语言出现的次数
时间: 2024-01-08 21:04:03 浏览: 21
可以使用Python中的collections模块中的defaultdict类来实现:
```python
from collections import defaultdict
language = ["Python", "C", "Scala", "Java", "Go", "Scala", "SQL", "PHP", "Python"]
language_counter = defaultdict(int)
for lang in language:
language_counter[lang] += 1
print(language_counter)
```
输出结果为:
```
defaultdict(<class 'int'>, {'Python': 2, 'C': 1, 'Scala': 2, 'Java': 1, 'Go': 1, 'SQL': 1, 'PHP': 1})
```
其中,defaultdict类也会返回一个字典,可以使用key进行访问,如果访问的key不存在,则会返回设定的默认值,这里默认值为int类型的0。使用+=操作符可以方便地实现计数器的功能。
相关问题
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:
```python
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。
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())
```