python 数据可视化

数据可视化

# -*- coding:utf-8 -*-
# 异常值处理
import pandas as pda
import numpy as npy
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as pyl
import io
数据可视化
# 评论数异常>100000，价格异常>1000
line = len(data.values)
col = len(data.values[0])
da = data.values
for i in range(0, line):
   for j in range(0, col):
      if (int(da[i][2]) > 1000):
# print(da[i])
          da[i][2] = data["价格"].mean()
      if (int(da[i][3]) > 100000):
# print(da[i])
          da[i][3] = data["评论"].mean()
          daF = pda.DataFrame(da)
          daF.sort_values(by=3)
          da2 = da.T
          price = da2[2]
          comt = da2[3]
          fig2 = pyl.figure()
          pyl.plot(price, comt, 'o')
          canvas = fig2.canvas
          buffer = io.BytesIO()
          canvas.print_png(buffer)
          img_spl2 = buffer.getvalue()
# print(data)s
          buffer.close()
          output['img_第二张散点图'] = img_spl2
def index(data):
# 输出结果必须为字典output
    output = {}
    data = pda.DataFrame(data[1:], columns=data[0])
# print(data)
    data.describe()
# 画散点图（横轴为价格，纵轴为评论数）
# 得到价格
    data2 = data.T
    price = data2.values[2]
# 得到评论数据
    fig = pyl.figure()
# print(price)
# print(comt)
    pyl.plot(price, comt, 'o')
    canvas = fig.canvas
    buffer = io.BytesIO()
    canvas.print_png(buffer)
    img_spl = buffer.getvalue()
# print(data)s
    buffer.close()
    output['img_第二张散点图'] = img_spl2
def index(data):
# 输出结果必须为字典output
    output = {}
    data = pda.DataFrame(data[1:], columns=data[0])
# print(data)
    data.describe()
# 画散点图（横轴为价格，纵轴为评论数）
# 得到价格
    data2 = data.T
    price = data2.values[2]
# 得到评论数据
    comt = data2.values[3]
    fig = pyl.figure()
# print(price)
    pyl.plot(price, comt, 'o')
    canvas = fig.canvas
    buffer = io.BytesIO()
    canvas.print_png(buffer)
    img_spl = buffer.getvalue()
# print(data)s
    buffer.close()
    output['img_散点图'] = img_spl
# pyl.show()
# pyl.show()
# 分布分析--直方图
    fre_price = dict()
    for num in da2[2]:
     fre_price[num] = fre_price.get(num, 0) + 1
     fre_price.keys()
     fre_price.values()
     pyl.bar(list(fre_price.keys()), list(fre_price.values()))
    print(output)
    return output
output['img_散点图'] = img_spl
# pyl.show()
# pyl.show()
# 分布分析--直方图
fre_price = dict()
for num in da2[2]:
fre_price[num] = fre_price.get(num, 0) + 1
fre_price.keys()
fre_price.values()
pyl.bar(list(fre_price.keys()), list(fre_price.values()))
print(output)
return output

标签：fre,python,data,price,buffer,可视化,print,pyl,数据
来源： https://www.cnblogs.com/wei23/p/10890685.html