# 计算 RFM 分数 def calculate_rfm(df): # Recency 分数(越小越好) df['R_Score'] = pd.qcut(df['Last_Login_Days_Ago'], q=5, labels=[5, 4, 3, 2, 1]) # Frequency 分数(越高越好) df['F_Score'] = pd.qcut(df['Purchase_Frequency'], q=5, labels=[1, 2, 3, 4, 5]) # ...
df.sort_values(by='利润',ascending=False) 如果需要自定义排序,可以将多个字段传入列表[ ]中,ascending用来自定义字段是升序还是降序排列,比如这里分别对“省份”,“销售额”两个字段降序排列。 df.sort_values(['省份','销售额'],ascending=[False,False]) 6. 分组聚合 分组聚合是数据处理中最常用的一个功...
select_dtypes() select_dtypes() 的作用是,基于 dtypes 的列返回数据帧列的一个子集。这个函数的参数可设置为包含所有拥有特定数据类型的列,亦或者设置为排除具有特定数据类型的列。 # We'll use the same dataframe that we used for read_csvframex = df.select...
# 每次调用函数,都返回一个新Series df["ymd"].str.replace("-", "").slice(0, 6) --- AttributeError Traceback (most recent call last) <ipython-input-13-ae278fb12255> in <module> 1 # 每次调用函数,都返回一个新Series ---> 2 df["ymd"].str.replace("-", "").slice(0, 6) d:...
A step-by-step Python code example that shows how to select rows from a Pandas DataFrame based on a column's values. Provided by Data Interview Questions, a mailing list for coding and data interview problems.
创建一个DataFrame对象,假设名为df,包含需要拆分的列: 代码语言:python 代码运行次数:0 复制Cloud Studio 代码运行 df = pd.DataFrame({'column_name': ['value1', 'value2', 'value3']}) 使用拆分符号将列拆分为多个子列,可以使用str.split()函数: ...
=df.loc[df['column_name'] != some_value]# isin返回一系列的数值,如果要选择不符合这个条件的数值使用~df.loc[~df['column_name'].isin(some_values)] import pandas as pd import numpy as npdf= pd.DataFrame({'A':'foo bar foo bar foo bar foo foo'.split(),'B':'one one two three ...
# 选取等于某些值的行记录 用 ==df.loc[df['column_name'] == some_value]# 选取某列是否是某一类型的数值 用 isindf.loc[df['column_name'].isin(some_values)]# 多种条件的选取 用 &df.loc[(df['column'] == some_value) &df['other_column'].isin(some_values)]# 选取不等于某些值的行...
Python program to select rows that do not start with some str in pandas# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d = {'col':['Harry','Carry','Darry','Jerry']} # Creating a DataFrame df = pd.DataFrame(d) ...
import ioimport requests# I am using this online data set just to make things easier foryou guysurl = "https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/datasets/AirPassengers.csv"s = requests.get(url).content# read only first 10 rowsdf = pd.read_csv(io.StringIO(s.decode(...