在Python中过滤数据集的首选方法是什么？

发布于 2025-01-30 01:46:45 字数 797 浏览 1 评论 0原文

滤波器数据集02 Telecom_usage.csv具有多种条件的

customercarecalls的值从偶数数字开始（2、4、6或8）
unanswerdCalls ＆gt; gt; blockedCalls

第一种方法：

df_2 = pd.read_csv('02 telecom_usage.csv')
df_3 = df_2[(df_2['CustomerCareCalls'].str.contains('^2|^4|^6|^8')&
                 df_2['UnansweredCalls']>df_2['BlockedCalls'])]

第二种方法：

df_4 = pd.read_csv('02 telecom_usage.csv')
df_5 = df_4[df_4['CustomerCareCalls'].str.contains('^2|^4|^6|^8')]
    df_6 = df_5[df_5['UnansweredCalls']>df_5['BlockedCalls']]]

我尝试使用同一数据集运行两个查询，但是在

第一个过滤器结果中，从5000个数据中的第一个过滤器结果多达160个数据，而结果的结果是第二个过滤器是5000个数据中的558个数据，

在我通过Excel手动检查结果后，结果都符合多个条件的请求。

我想知道，为什么结果有所不同

原文

Filter dataset 02 telecom_usage.csv with multiple condition

the value of CustomerCareCalls starting with even digits (2, 4, 6 or 8)
the value of UnansweredCalls > BlockedCalls

First way:

df_2 = pd.read_csv('02 telecom_usage.csv')
df_3 = df_2[(df_2['CustomerCareCalls'].str.contains('^2|^4|^6|^8')&
                 df_2['UnansweredCalls']>df_2['BlockedCalls'])]

second way:

df_4 = pd.read_csv('02 telecom_usage.csv')
df_5 = df_4[df_4['CustomerCareCalls'].str.contains('^2|^4|^6|^8')]
    df_6 = df_5[df_5['UnansweredCalls']>df_5['BlockedCalls']]]

I've tried to run both queries with same dataset, but there are different results in both

the first filter results as many as 160 data from 5000 data, while the results of the second filter are 558 data from 5000 data

after I checked the results manually via excel, the results both met the request for multiple conditions.

I want to know, why the result is difference

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