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<>pandas模块
pandas是一个强大的分析结构化数据的工具集；它的使用基础是Numpy（提供高性能的矩阵运算）；用于数据挖掘和数据分析，同时也提供数据清洗功能。
Pandas中常见的数据结构有两种：
SeriesDateFrame
类似一维数组的对象，类似多维数组/表格数组；每列数据可以是不同的类型；索引包括列索引和行索引。
Series
* 构建Series：ser_obj = pd.Series(range(10))
* 由索引和数据组成（索引在左<自动创建的>，数据在右）。
* 获取数据和索引：ser_obj.index; ser_obj.values
* 预览数据： ser_obj.head(n);ser_obj.tail(n)
DateFrame
* 获取列数据：df_obj[col_idx]或df_obj.col_idx
* 增加列数据：df_obj[new_col_idx] = data
* 删除列：del df_obj[col_idx]
* 按值排序：sort_values(by = “label_name”)
常用方法
Count非NA值得数量
describe针对Series或各DataFrame列计算汇总统计
min\max计算最小值和最大值
argmin\argmax计算能够获取到最大值或最小值的索引位置
idxmin\idxmax计算能够获取到最小值和最大值的索引值
quantile计算样本的分位数（0-1）
sum值得总和
mean值得平均值
median值的算术中位数（50%分位数）
mad根据平均值计算平均绝对离差
var样本值得方差
std样本值得标准差
skew样本值的偏度（三阶距）
kurt样本值的峰度（四阶距）
cumsum样本值的累计和
cummin\cummax样本值的累计最大值和累计最小值
cumprod样本值的累计积
diff计算一阶差分（对时间序列很有用）
pct_change计算百分数变化
处理缺失数据
* Dropna()丢弃缺失数据
* Fillna()填充缺失数据
数据过滤
Df[filter_condition]依据filter_condition(条件)对Df(数据)进行过滤。
绘图功能
Plot(kind,x,y,title,figsize)
Kind(绘制什么形式的图),x（x轴内容）,y（y轴内容）,title（图标题）,figsize（图大小）
保存图片：plt.savefig()
（"The fool doth think he is wise, but the wise man knows himself to be a
fool." --威廉·莎士比亚）
