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 pd
pd.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: int64

describe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