英文:
Extract only natural numbers belonging to a specific column, for every group/id in Python
问题
# 加载所需的库import pandas as pd# 创建数据集data = {'id': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,2, 2, 2, 2, 2, 2, 2,3, 3, 3, 3, 3, 3, 3, 3,4, 4, 4, 4, 4, 4,5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],'cycle': [0.0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2,0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2,0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4,0.0, 0.2, 0.4, 0.6, 0.8, 1.0,0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2],'Salary': [6, 7, 7, 7, 8, 9, 10, 11, 12, 13, 14, 15,3, 4, 4, 4, 4, 5, 6,2, 8, 9, 10, 11, 12, 13, 14,1, 8, 9, 10, 11, 12,6, 7, 7, 9, 10, 11, 12, 13, 14, 15, 9, 10, 11, 12, 13, 14, 15],'Children': ['Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes', 'No','Yes', 'Yes', 'Yes', 'No', 'Yes', 'Yes', 'Yes','Yes', 'No', 'Yes', 'Yes', 'No', 'No', 'Yes', 'Yes','Yes', 'Yes', 'No', 'Yes', 'Yes', 'Yes','Yes', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes'],'Days': [141, 123, 128, 66, 66, 120, 141, 52, 96, 120, 141, 52,141, 96, 120, 120, 141, 52, 96,141, 15, 123, 128, 66, 120, 141, 141,141, 141, 123, 128, 66, 67,141, 123, 128, 66, 123, 128, 66, 120, 141, 52, 123, 128, 66, 123, 128, 66, 120]}# 转换为数据框df = pd.DataFrame(data)print("\n df = \n", df)
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英文:
#Load the required librariesimport pandas as pd#Create datasetdata = {'id': [1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1,2, 2, 2, 2, 2, 2, 2,3, 3, 3, 3, 3, 3, 3,3,4, 4, 4, 4, 4,4,5, 5, 5, 5, 5, 5,5, 5, 5,5, 5,5, 5,5, 5, 5,5],'cycle': [0.0, 0.2,0.4, 0.6, 0.8, 1,1.2,1.4,1.6,1.8,2.0,2.2,0.0, 0.2,0.4, 0.6,0.8,1.0,1.2,0.0, 0.2,0.4, 0.6, 0.8,1.0,1.2,1.4,0.0, 0.2,0.4, 0.6, 0.8,1.0,0.0, 0.2,0.4, 0.6, 0.8, 1.0,1.2,1.4,1.6,1.8, 2.0,2.2,2.4,2.6,2.8,3.0,3.2],'Salary': [6, 7, 7, 7,8,9,10,11,12,13,14,15,3, 4, 4, 4,4,5,6,2, 8,9,10,11,12,13,14,1, 8,9,10,11,12,6, 7, 7,9,10,11,12,13,14,15, 9,10,11,12,13,14,15],'Children': ['Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No','No', 'Yes', 'Yes', 'Yes', 'No','Yes', 'Yes', 'Yes', 'No', 'Yes', 'Yes', 'Yes','Yes', 'No','Yes', 'Yes', 'No','No', 'Yes','Yes','Yes', 'Yes', 'No','Yes', 'Yes','Yes','Yes', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes'],'Days': [141, 123, 128, 66, 66, 120, 141, 52,96, 120, 141, 52,141, 96, 120,120, 141, 52,96,141, 15,123, 128, 66, 120, 141, 141,141, 141,123, 128, 66,67,141, 123, 128, 66, 123, 128, 66, 120, 141, 52, 123, 128, 66, 123, 128, 66, 120,],}#Convert to dataframedf = pd.DataFrame(data)print("\n df = \n",df)
The above dataset looks as such:
Here for every 'id', I wish to extract only those rows, whose 'cycle' is an natural number, as shown in the boxes of the above image file.
The expected dataframe is as such:
Can somebody please let me know how to achieve this task in Pyhton?
答案1
得分: 1
筛选出不等于0的值，并比较整数值是否与浮点数值相同：
out = df[df['cycle'].ne(0) & df['cycle'].astype(int).eq(df['cycle'])].reset_index(drop=True)print(out)id cycle Salary Children Days0 1 1.0 9 Yes 1201 2 1.0 5 Yes 522 3 1.0 12 No 1203 4 1.0 12 Yes 674 5 1.0 11 Yes 1285 5 2.0 9 No 1236 5 3.0 14 Yes 66
英文:
Filter out 0 values and compare if integer values is same like float value:
out=df[df['cycle'].ne(0) & df['cycle'].astype(int).eq(df['cycle'])].reset_index(drop=True)print (out)id cycle Salary Children Days0 1 1.0 9 Yes 1201 1 2.0 14 Yes 1412 2 1.0 5 Yes 523 3 1.0 12 No 1204 4 1.0 12 Yes 675 5 1.0 11 Yes 1286 5 2.0 9 No 1237 5 3.0 14 Yes 66
答案2
得分: 1
这是另一种方法：
df[(df['cycle']!=0.0) & (df['cycle']%0.5==0)].reset_index()
英文:
Here's another way:
df[(df['cycle']!=0.0) & (df['cycle']%0.5==0)].reset_index()
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