速战速决 Python - python 第三方库（pandas）: DataFrame基础
速战速决 Python https://github.com/webabcd/PythonSample
作者 webabcd
速战速决 Python - python 第三方库（pandas）: DataFrame基础
示例如下:
thirdLib/pandas/sample2.py
# pandas - DataFrame 基础
# Series - 带索引的一维表
# DataFrame - 带索引的二维表
#
# 注 pandas 是基于 numpy 的
import pandas as pd
index1 = ['zhao', 'qian', 'sun', 'li', 'zhou']
data1 = {
"age": [40, 25, 22, 28, 28],
"city": ['beijing', 'beijing', 'shanghai', 'beijing', 'shanghai']
}
# 实例化 DataFrame（指定索引列，包括列名的数据列），不指定索引的话默认就是从 0 开始的序列
a = pd.DataFrame(data=data1, index=index1)
print(a)
'''
第一列是索引列，右面的都是数据列
age city
zhao 40 beijing
qian 25 beijing
sun 22 shanghai
li 28 beijing
zhou 28 shanghai
'''
index2 = pd.Index(['zhao', 'qian', 'sun', 'li', 'zhou'], dtype=object)
data2 = [[40,"beijing"], [25,"beijing"], [22,"shanghai"], [28,"beijing"], [28,"shanghai"]]
# 实例化 DataFrame（指定索引列和索引列的数据类型，不包括列名的数据列，数据列的列名列表），不指定索引的话默认就是从 0 开始的序列
b = pd.DataFrame(data=data2, index=index2, columns=["age", "city"])
print(b)
'''
第一列是索引列，右面的都是数据列
age city
zhao 40 beijing
qian 25 beijing
sun 22 shanghai
li 28 beijing
zhou 28 shanghai
'''
# 获取数据的行数和列数
print(b.shape) # (5, 2)
# 获取数据列的数据
print(b.values)
'''
[[40 'beijing']
[25 'beijing']
[22 'shanghai']
[28 'beijing']
[28 'shanghai']]
'''
# 获取索引列的数据
print(list(b.index)) # ['zhao', 'qian', 'sun', 'li', 'zhou']
# 行列转换
print(b.T)
'''
zhao qian sun li zhou
age 40 25 22 28 28
city beijing beijing shanghai beijing shanghai
'''
# 获取指定列的数据
print(b.get('age'))
print(b.age)
'''
zhao 40
qian 25
sun 22
li 28
zhou 28
'''
# 获取指定列的指定索引的数据
print(b.get('age').get('zhao')) # 40
print(b.age.zhao) # 40
# 取指定列最大值的索引列的值
print(b.age.idxmax()) # zhao
# 取指定列最小值的索引列的值
print(b.age.idxmin()) # sun
# 取指定列的最大值
print(b.age.max()) # 40
# 取指定列的最小值
print(b.age.min()) # 22
# 取指定列的平均值
print(b.age.mean()) # 28.6
# 取指定列的累加值
print(b.age.sum()) # 143
# 取指定列的中位数
print(b.age.quantile()) # 28.0
# 取指定列的标准差
print(b.age.std()) # 6.841052550594829
# 对指定的列做累加求和（返回一个 Series 类型的数据）
print(b.age.cumsum())
'''
zhao 40
qian 65
sun 87
li 115
zhou 143
'''
# 获取指定索引的数据
print(b.loc['zhao']) # 返回的是 Series 对象
'''
age 40
city beijing
'''
# 获取指定位置的数据
print(b.iloc[0]) # 返回的是 Series 对象
'''
age 40
city beijing
'''
# 获取指定索引的指定列的数据
print(b.loc['zhao'].city) # beijing
print(b.loc['zhao'].get('city')) # beijing
print(a.loc['zhao'][['age', 'city']])
'''
age 40
city beijing
'''
# 获取指定索引的行的最大值（前提是这行都是数字类型的）。类似的还有 min(), idxmax(), idxmin(), mean(), sum(), quantile(), std(), cumsum() 之类的，上面都写了
# print(b.loc['zhao'].max())
# 为 DataFrame 添加列，并为其指定相同的值
b["gender"] = "M"
'''
age city gender
zhao 40 beijing M
qian 25 beijing M
sun 22 shanghai M
li 28 beijing M
zhou 28 shanghai M
'''
print(b)
# 为 DataFrame 添加列，并为其指定不同的值
b["salary"] = [100, 200, 150, 150, 150]
print(b)
'''
age city gender salary
zhao 40 beijing M 100
qian 25 beijing M 200
sun 22 shanghai M 150
li 28 beijing M 150
zhou 28 shanghai M 150
'''
# 为 DataFrame 添加列，并为其指定与原有信息相关的值
b = b.assign(salary_expected=b["salary"] * 2)
print(b)
'''
age city gender salary salary_expected
zhao 40 beijing M 100 200
qian 25 beijing M 200 400
sun 22 shanghai M 150 300
li 28 beijing M 150 300
zhou 28 shanghai M 150 300
'''
# 为 DataFrame 添加列，并为其指定与原有信息相关的值（通过调用函数的方式）
def getLevel(age, salary):
# 注意：这里的 age 和 salary 是 Series 类型的数据
print(type(age), type(salary)) # <class 'pandas.core.series.Series'> <class 'pandas.core.series.Series'>
return (age * 0.5 + salary * 2) / 50
b = b.assign(level=getLevel(b["age"], b["salary"]))
print(b)
'''
age city gender salary salary_expected level
zhao 40 beijing M 100 200 4.40
qian 25 beijing M 200 400 8.25
sun 22 shanghai M 150 300 6.22
li 28 beijing M 150 300 6.28
zhou 28 shanghai M 150 300 6.28
'''
# 删除指定的列
b = b.drop(["salary_expected"], axis=1)
print(b)
'''
age city gender salary level
zhao 40 beijing M 100 4.40
qian 25 beijing M 200 8.25
sun 22 shanghai M 150 6.22
li 28 beijing M 150 6.28
zhou 28 shanghai M 150 6.28
'''
# 修改列名
b = b.rename(columns={"level": "employee_level"})
print(b)
'''
age city gender salary employee_level
zhao 40 beijing M 100 4.40
qian 25 beijing M 200 8.25
sun 22 shanghai M 150 6.22
li 28 beijing M 150 6.28
zhou 28 shanghai M 150 6.28
'''
# 删除指定的行
b = b.drop(["zhou"], axis=0)
print(b)
'''
age city gender salary employee_level
zhao 40 beijing M 100 4.40
qian 25 beijing M 200 8.25
sun 22 shanghai M 150 6.22
li 28 beijing M 150 6.28
'''
# 替换指定字段的指定的值为另一个指定的值
b = b.replace({"city": 'beijing'}, 'bj')
print(b)
'''
age city gender salary employee_level
zhao 40 bj M 100 4.40
qian 25 bj M 200 8.25
sun 22 shanghai M 150 6.22
li 28 bj M 150 6.28
'''
# 所有数据都变为大写
print(a.applymap(lambda x: str(x).upper()))
'''
age city
zhao 40 BEIJING
qian 25 BEIJING
sun 22 SHANGHAI
li 28 BEIJING
zhou 28 SHANGHAI
'''
# 所有数据都乘以 2
def temp(x):
return x * 2
print(a.applymap(lambda x: temp(x)))
'''
age city
zhao 80 beijingbeijing
qian 50 beijingbeijing
sun 44 shanghaishanghai
li 56 beijingbeijing
zhou 56 shanghaishanghai
'''
# 修改索引名
b = b.rename(index={"zhao": "wang"})
print(b)
'''
age city gender salary employee_level
wang 40 bj M 100 4.40
qian 25 bj M 200 8.25
sun 22 shanghai M 150 6.22
li 28 bj M 150 6.28
'''
速战速决 Python https://github.com/webabcd/PythonSample
作者 webabcd
