网站建设定制公众号小程序,大型电子商务网站建设,工程综合承包,网站模板怎么使用定量数据本质上是数值#xff0c;应该是衡量某样东西的数量。 定性数据本质上是类别#xff0c;应该是描述某样东西的性质。
全部的数据列如下#xff0c;其中既有定性列也有定量列#xff1b;
import pandas as pdpd.options.display.max_columns None
pd.set_option(e…定量数据本质上是数值应该是衡量某样东西的数量。 定性数据本质上是类别应该是描述某样东西的性质。
全部的数据列如下其中既有定性列也有定量列
import pandas as pdpd.options.display.max_columns None
pd.set_option(expand_frame_repr, False)
salary_ranges pd.read_csv(./data/Salary_Ranges_by_Job_Classification.csv)
print(salary_ranges.head())
# SetID JobCode Eff Date SalEndDate SalarySetID SalPlan Grade Step BiweeklyHighRate BiweeklyLowRate UnionCode ExtendedStep PayType
# 0 COMMN 109 07/01/2009 12:00:00 AM 06/30/2010 12:00:00 AM COMMN SFM 0 1 $0.00 $0.00 330 0 C
# 1 COMMN 110 07/01/2009 12:00:00 AM 06/30/2010 12:00:00 AM COMMN SFM 0 1 $15.00 $15.00 323 0 D
# 2 COMMN 111 07/01/2009 12:00:00 AM 06/30/2010 12:00:00 AM COMMN SFM 0 1 $25.00 $25.00 323 0 D
# 3 COMMN 112 07/01/2009 12:00:00 AM 06/30/2010 12:00:00 AM COMMN SFM 0 1 $50.00 $50.00 323 0 D
# 4 COMMN 114 07/01/2009 12:00:00 AM 06/30/2010 12:00:00 AM COMMN SFM 0 1 $100.00 $100.00 323 0 M
.info()可以了解数据的列信息以及每列非null的行数
print(salary_ranges.info())# class pandas.core.frame.DataFrame
# RangeIndex: 1356 entries, 0 to 1355
# Data columns (total 13 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 SetID 1356 non-null object
# 1 Job Code 1356 non-null object
# 2 Eff Date 1356 non-null object
# 3 Sal End Date 1356 non-null object
# 4 Salary SetID 1356 non-null object
# 5 Sal Plan 1356 non-null object
# 6 Grade 1356 non-null object
# 7 Step 1356 non-null int64
# 8 Biweekly High Rate 1356 non-null object
# 9 Biweekly Low Rate 1356 non-null object
# 10 Union Code 1356 non-null int64
# 11 Extended Step 1356 non-null int64
# 12 Pay Type 1356 non-null object
# dtypes: int64(3), object(10)
# memory usage: 137.8 KB
# None也可以使用以下方法更快速的计算缺失值的信息
print(salary_ranges.isnull().sum())
# SetID 0
# Job Code 0
# Eff Date 0
# Sal End Date 0
# Salary SetID 0
# Sal Plan 0
# Grade 0
# Step 0
# Biweekly High Rate 0
# Biweekly Low Rate 0
# Union Code 0
# Extended Step 0
# Pay Type 0
# dtype: int64describe方法查看定量数据的描述性统计Pandas认为数据只有3个定量列Step、Union Code和Extended Step步进、工会代码和增强步进。先不说步进和增强步进很明显工会代码不是定量的。虽然这一列是数但这些数不代表数量只代表某个工会的代码
print( salary_ranges.describe())# Step Union Code Extended Step
# count 1356.000000 1356.000000 1356.000000
# mean 1.294985 392.676991 0.150442
# std 1.045816 338.100562 1.006734
# min 1.000000 1.000000 0.000000
# 25% 1.000000 21.000000 0.000000
# 50% 1.000000 351.000000 0.000000
# 75% 1.000000 790.000000 0.000000
# max 5.000000 990.000000 11.000000最值得注意的特征是一个定量列Biweekly High Rate双周最高工资和一个定性列Grade工作种类
salary_ranges salary_ranges[[BiweeklyHighRate, Grade]]
print(salary_ranges.head())# BiweeklyHighRate Grade
# 0 $0.00 0
# 1 $15.00 0
# 2 $25.00 0
# 3 $50.00 0
# 4 $100.00 0查看两个字段的类型
print(salary_ranges.info())# class pandas.core.frame.DataFrame
# RangeIndex: 1356 entries, 0 to 1355
# Data columns (total 2 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 BiweeklyHighRate 1356 non-null object
# 1 Grade 1356 non-null object
# dtypes: object(2)
# memory usage: 21.3 KB
# None我们清理一下数据移除工资前面的美元符号保证数据类型正确。当处理定量数据时一般使用整数或浮点数作为类型最好使用浮点数定性数据则一般使用字符串或Unicode对象。
salary_ranges[BiweeklyHighRate] salary_ranges[BiweeklyHighRate].map(lambda value:value.replace($,))
print(salary_ranges.head())# BiweeklyHighRate Grade
# 0 0.00 0
# 1 15.00 0
# 2 25.00 0
# 3 50.00 0
# 4 100.00 0数据类型并没有变
print(salary_ranges.info())
# class pandas.core.frame.DataFrame
# RangeIndex: 1356 entries, 0 to 1355
# Data columns (total 2 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 BiweeklyHighRate 1356 non-null object
# 1 Grade 1356 non-null object
# dtypes: object(2)
# memory usage: 21.3 KB
# None将BiweeklyHighRate和Grade列中的数据分别转换为浮点数、字符串
salary_ranges[BiweeklyHighRate] salary_ranges[BiweeklyHighRate].astype(float)
salary_ranges[Grade] salary_ranges[Grade].astype(str)
print(salary_ranges.info())# class pandas.core.frame.DataFrame
# RangeIndex: 1356 entries, 0 to 1355
# Data columns (total 2 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 BiweeklyHighRate 1356 non-null float64
# 1 Grade 1356 non-null object
# dtypes: float64(1), object(1)
# memory usage: 21.3 KB
# None
