英文:
Python -Pandas- How to return speicfic rows based on conditions and return the specific column rows only
问题
以下是代码的翻译部分：
有一个数据框，根据多个条件需要返回行，并创建一个新列来存储这些行。
示例：
记录 = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya'],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78],
'hours': [1, 2, 3, 4, 5, 6]
}
条件如下：如果年龄为19和21，流为数学和商业，则返回包括其他记录在内的小时，并将这些小时存储在新创建的列中。
示例输出：
新列添加为`new_column`
Name Age Stream Percentage hours new_column
Ankit 21 Math 88 1 1
Amit 19 Commerce 92 2 2
Aishwarya 20 Science 95 3 0
Priyanka 18 Science 70 4 0
Priya 17 Math 65 5 0
Shaurya 21 Science 78 6 0
在`new_column`中的0值表示过滤条件不满足。
尝试了以下代码，但结果不如预期，而且不是一个简化版本。
条件如下：
```python
options1 = ['Math', 'Commerce']
options2 = [21, 19]
dataframe1 = dataframe[(dataframe['Stream'].isin(options1)) & (dataframe['Age'].isin(options2))]
dataframe1['new_column'] = dataframe1['hours']
dataframe = pd.merge(dataframe, dataframe1, on='Name', how='left')
还尝试了以下代码：
dataframe['New'] = dataframe['hours']
dataframe_bkp.loc[:, ['New']] = dataframe_bkp[['Stream', 'Age', 'New']].apply(lambda x: 0 if (x.Stream in ['Maths', 'Commerce'] and (x.Age in [19, 21])) else dataframe_bkp['New'], axis=1)
希望这些翻译对你有所帮助。
英文:
Have a dateframe and based on muliple conditions need to return the rows and to create a new column to store these rows.
Example
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78],hours=[1,2,3,4,5,6}
Condition like : If age is in 19 & 21 , Stream in Maths, Commerce then return hours along with the other records and these hours to be stored in a new column created for the rows returned
Example: Output:
New column added = new_column
Name Age Stream Percentage hours new_column
Ankit 21 Math 88 1 1
Amit 19 Commerce 92 2 2
Aishwarya 20 Science 95 3 0
Priyanka 18 Science 70 4 0
Priya 17 English 65 5 0
Shaurya 21 Science 78 6 0
The 0 value in new_column since the filter conditions arent satisfied.
Tried below code , but the results not as expected and not a simplified version.
- Conditions:
options1 = ['Math', 'Commerce']
options2 = [21,19]
dataframe1=dataframe[(dataframe['Stream'].isin(options)) & (dataframe['Age'].isin(options2))]
dataframe1['new_column']=dataframe1['hours']
dataframe=pd.merge(dataframe,dataframe1,on='Name',how='left')
Also tried with below code:
dataframe['New']=dataframe['hours']
dataframe_bkp.loc[:,['New']] = dataframe_bkp[['Stream','Age','New']].apply(lambda x: 0 if (x.Stream in
['Maths','Commerce'] & (x.Age in [19,21] ) else dataframe_bkp['New'],axis=1 )
答案1
得分: 2
使用 Series.where：
df['new_column'] = df['hours'].where(df['Age'].between(19, 21) & df['Stream'].isin(['Math', 'Commerce']), 0)
替代方法：
import numpy as np
m1 = df['Age'].between(19, 21)
m2 = df['Stream'].isin(['Math', 'Commerce'])
df['new_column'] = np.where(m1 & m2, df['hours'], 0)
输出：
Name Age Stream Percentage hours new_column
0 Ankit 21 Math 88 1 1
1 Amit 19 Commerce 92 2 2
2 Aishwarya 20 Science 95 3 0
3 Priyanka 18 Math 70 4 0
4 Priya 17 Math 65 5 0
5 Shaurya 21 Science 78 6 0
英文:
Use Series.where:
df['new_column'] = df['hours'].where( df['Age'].between(19, 21)
& df['Stream'].isin(['Math', 'Commerce']),
0)
Alternative:
import numpy as np
m1 = df['Age'].between(19, 21)
m2 = df['Stream'].isin(['Math', 'Commerce'])
df['new_column'] = np.where(m1&m2, df['hours'], 0)
Output:
Name Age Stream Percentage hours new_column
0 Ankit 21 Math 88 1 1
1 Amit 19 Commerce 92 2 2
2 Aishwarya 20 Science 95 3 0
3 Priyanka 18 Math 70 4 0
4 Priya 17 Math 65 5 0
5 Shaurya 21 Science 78 6 0
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