[TOC] # 构造一个DataFrame结构 ~~~ import pandas as pd data = {'country': ['aaa', 'bbb', 'ccc'], 'population': [10, 12, 14]} frame = pd.DataFrame(data) print(frame) ~~~ 输出 ~~~ country population 0 aaa 10 1 bbb 12 2 ccc 14 ~~~ 构造出来的DataFrame也能打印出info这些信息 # 取指定的数据 cvs是这样  我们取出里面的Age值 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') print(csv['Age']) ~~~ 输出 左侧0,1,2,3是索引  这些行有空值 如果要取出前5个 `print(csv['Age'][:5])` # 指定索引 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') # 设置左侧索引 index = csv.set_index('Name') print(index['Age'][:5]) ~~~ 输出 ~~~ Name Braund, Mr. Owen Harris 22.0 Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38.0 Heikkinen, Miss. Laina 26.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 Allen, Mr. William Henry 35.0 Name: Age, dtype: float64 ~~~ # 根据索引找对应的值 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') # 设置左侧索引 index = csv.set_index('Name') # 根据名字这个索引找他的年龄 # 索引和年龄 age = index['Age'] print(age['Braund, Mr. Owen Harris']) ~~~ 输出 ~~~ 22.0 ~~~ # 运算 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') # 设置左侧索引 index = csv.set_index('Name') # 索引和年龄 age = index['Age'] age1 = age * 10 print(age1) ~~~ 输出 ~~~ Name Braund, Mr. Owen Harris 22.0 Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38.0 Heikkinen, Miss. Laina 26.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 Allen, Mr. William Henry 35.0 Moran, Mr. James NaN ~~~ 他的运算不影响原来的值,运算的结果需要新的值类来接收 # 求最小值,最大值,mean值 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') # 设置左侧索引 index = csv.set_index('Name') # 索引和年龄 age = index['Age'] # 求最小值 age_min = age.min() print(age_min) # 求最大值 age_max = age.max() print(age_max) # 求mean值 mean = age.mean() print(mean) ~~~ 输出 ~~~ 0.42 80.0 29.6991176471 ~~~ # 观察数据的基本统计特性 ~~~ import pandas as pd csv = pd.read_csv('./titanic.csv') describe = csv.describe() print(describe) ~~~ 输出