python（一）——pandas

1、查看帮助文档

import matplotlib.pyplot as plt
help(plt.plot)

 2、arr[:,],“：”表示全选每行，“，”后再选取指定的列

ma = np.array([[1,3,2],[3,4,6],[3,33,6]])
ma.shape
print (ma[:,2])
print (ma[:,1:3])

 3、pandas中的DataFrame基本操作

构造函数

方法	描述
DataFrame([data, index, columns, dtype, copy])	构造数据框

属性和数据

方法	描述
Axes	index: row labels；columns: column labels
DataFrame.as_matrix([columns])	转换为矩阵
DataFrame.dtypes	返回数据的类型
DataFrame.ftypes	Return the ftypes (indication of sparse/dense and dtype) in this object.
DataFrame.get_dtype_counts()	返回数据框数据类型的个数
DataFrame.get_ftype_counts()	Return the counts of ftypes in this object.
DataFrame.select_dtypes([include, exclude])	根据数据类型选取子数据框
DataFrame.values	Numpy的展示方式
DataFrame.axes	返回横纵坐标的标签名
DataFrame.ndim	返回数据框的纬度
DataFrame.size	返回数据框元素的个数
DataFrame.shape	返回数据框的形状
DataFrame.memory_usage([index, deep])	Memory usage of DataFrame columns.

描述统计学

方法	描述
DataFrame.abs()	返回绝对值
DataFrame.all([axis, bool_only, skipna, level])	Return whether all elements are True over requested axis
DataFrame.any([axis, bool_only, skipna, level])	Return whether any element is True over requested axis
DataFrame.clip([lower, upper, axis])	Trim values at input threshold(s).
DataFrame.clip_lower(threshold[, axis])	Return copy of the input with values below given value(s) truncated.
DataFrame.clip_upper(threshold[, axis])	Return copy of input with values above given value(s) truncated.
DataFrame.corr([method, min_periods])	返回本数据框成对列的相关性系数
DataFrame.corrwith(other[, axis, drop])	返回不同数据框的相关性
DataFrame.count([axis, level, numeric_only])	返回非空元素的个数
DataFrame.cov([min_periods])	计算协方差
DataFrame.cummax([axis, skipna])	Return cumulative max over requested axis.
DataFrame.cummin([axis, skipna])	Return cumulative minimum over requested axis.
DataFrame.cumprod([axis, skipna])	返回累积
DataFrame.cumsum([axis, skipna])	返回累和
DataFrame.describe([percentiles, include, …])	整体描述数据框
DataFrame.diff([periods, axis])	1st discrete difference of object
DataFrame.eval(expr[, inplace])	Evaluate an expression in the context of the calling DataFrame instance.
DataFrame.kurt([axis, skipna, level, …])	返回无偏峰度Fisher’s (kurtosis of normal == 0.0).
DataFrame.mad([axis, skipna, level])	返回偏差
DataFrame.max([axis, skipna, level, …])	返回最大值
DataFrame.mean([axis, skipna, level, …])	返回均值
DataFrame.median([axis, skipna, level, …])	返回中位数
DataFrame.min([axis, skipna, level, …])	返回最小值
DataFrame.mode([axis, numeric_only])	返回众数
DataFrame.pct_change([periods, fill_method, …])	返回百分比变化
DataFrame.prod([axis, skipna, level, …])	返回连乘积
DataFrame.quantile([q, axis, numeric_only, …])	返回分位数
DataFrame.rank([axis, method, numeric_only, …])	返回数字的排序
DataFrame.round([decimals])	Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, …])	返回无偏标准误
DataFrame.skew([axis, skipna, level, …])	返回无偏偏度
DataFrame.sum([axis, skipna, level, …])	求和
DataFrame.std([axis, skipna, level, ddof, …])	返回标准误差
DataFrame.var([axis, skipna, level, ddof, …])	返回无偏误差