Python - 使用 numpy 过滤 Pandas DataFrame
pythonserver side programmingprogramming更新于 2025/10/27 23:40:17
numpy where() 方法可用于过滤 Pandas DataFrame。在 where() 方法中提及条件。首先，让我们导入所需的库及其各自的别名
import pandas as pd import numpy as np
我们现在将创建一个包含产品记录的 Pandas DataFrame
dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],"Opening_Stock": [300, 700, 1200, 1500],"Closing_Stock": [200, 500, 1000, 900]})
使用numpy where() 使用 2 个条件过滤 DataFrame
resValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000)) print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues1]
让我们再次使用 numpy where() 使用 3 个条件过滤 DataFrame
resValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000)& (dataFrame['Product'].str.startswith('C')))
示例
以下是完整代码
import pandas as pd
import numpy as np
dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],"Opening_Stock": [300, 700, 1200, 1500],"Closing_Stock": [200, 500, 1000, 900]})
print"DataFrame...\n",dataFrame
# 使用 numpy where() 过滤 DataFrame，条件为 2 个
resValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000))
print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues1]
# 使用 numpy where() 过滤 DataFrame，条件为 3 个
resValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) & (dataFrame['Product'].str.startswith('C')))
print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues2]
输出
这将产生以下输出
DataFrame... Closing_Stock Opening_Stock Product 0 200 300 SmartTV 1 500 700 ChromeCast 2 1000 1200 Speaker 3 900 1500 Earphone Filtered DataFrame Value = Closing_Stock Opening_Stock Product 1 500 700 ChromeCast 3 900 1500 Earphone Filtered DataFrame Value = Closing_Stock Opening_Stock Product 1 500 700 ChromeCast
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python 参考教程 - 该教程包含有关 python 的更多信息:https://www.cainiaomax.com/python/
