总算找到时间学习了下pandas，先学习一部分，后面继续补充
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 创建序列, 让pandas创建默认整数索引
s = pd.Series([1, 3, 5, np.nan, 6, 8])
print(s)
"""
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
"""
# 数据读取
# csv文件读取
# 默认第一行列名，数据从第二行起
# (数据从第一行起header=None)
data = pd.read_csv("data.csv")
print(data)
"""
id name age score
0 1 tom 12 98
1 2 tom 12 98
2 3 tom 12 98
3 4 tom 12 98
4 5 tom 12 98
5 6 tom 12 98
6 7 tom 12 98
7 8 tom 12 98
8 9 tom 12 98
9 10 tom 12 98
"""
# 显示前面几行数据（默认前5行）
print(data.head())
"""
id name age score
0 1 tom 12 98
1 2 tom 12 98
2 3 tom 12 98
3 4 tom 12 98
4 5 tom 12 98
"""
# 读取尾部数据
print(data.tail())
"""
id name age score
5 6 tom 12 98
6 7 tom 12 98
7 8 tom 12 98
8 9 tom 12 98
9 10 tom 12 98
"""
# 显示列名
print(data.columns)
# Index(['id', 'name', 'age', 'score'], dtype='object')
# 显示行号
print(data.index)
# RangeIndex(start=0, stop=10, step=1)
# 显示大小
print(data.shape)
# (10, 4)
# 索引和计算
# 列名默认为字符串(Object)。行的index默认为整数
# 提取行
print(data.loc[3:6])
"""
id name age score
3 4 tom 12 98
4 5 tom 12 98
5 6 tom 12 98
6 7 tom 12 98
"""
# 使用list提取行
print(data[3:6])
"""
id name age score
3 4 tom 12 98
4 5 tom 12 98
5 6 tom 12 98
"""
# 提取列
columns = ["name", "age"]
print(data[columns])
"""
name age
0 tom 12
1 tom 12
2 tom 12
3 tom 12
4 tom 12
5 tom 12
6 tom 12
7 tom 12
8 tom 12
9 tom 12
"""
# 列名转为list
print(data.columns.tolist())
# ['id', 'name', 'age', 'score']
print(data.columns.values)
# ['id' 'name' 'age' 'score']
print(data.columns.values.dtype)
# object
# 切片
print(data.loc[3:6][["name", "age"]])
"""
name age
3 tom 12
4 tom 12
5 tom 12
6 tom 12
"""
print(data["name"])
"""
0 tom
1 tom
2 tom
3 tom
4 tom
5 tom
6 tom
7 tom
8 tom
9 tom
Name: name, dtype: object
"""
# 取极值
print(data["id"].max())
# 10
# 运算
num = data.shape[0]
age_average = data["id"]/num
print(age_average.head())
"""
0 0.1
1 0.2
2 0.3
3 0.4
4 0.5
Name: id, dtype: float64
"""
# 排序
data.sort_values("id", inplace=False, ascending=False)
print(data.head())
