数据分析

Posted 2020-11-12 秋雨秋雨秋雨

基本统计：

from pandas import read_csv;

data = read_csv(\'D:\\\\PA\\\\8.1\\\\data.csv\')

data.score.describe()

data.score.size

data.score.max();

data.score.min;

data.score.sum;

data.score.mean;

data.score.var;

data.score.std;

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分组分析：

import numpy;
from pandas import read_csv;

data = read_csv(\'C:\\\\PA\\\\8.2\\\\data.csv\');
data[\'score2\'] = data[\'score\']*2

data.groupby(by=[\'class\'])[\'score\'].agg({
    \'总分\':numpy.sum, 
    \'人数\':numpy.size, 
    \'平均值\':numpy.mean, 
    \'方差\':numpy.var, 
    \'标准差\':numpy.std
})

data.groupby(by=[\'class\', \'name\'])[[\'score\', \'score2\']].agg([
    numpy.size, 
    numpy.sum
])

result = data.groupby(by=[\'class\'])[\'score\'].agg({
    \'总分\':numpy.sum, 
    \'人数\':numpy.size, 
    \'平均值\':numpy.mean, 
    \'方差\':numpy.var, 
    \'标准差\':numpy.std
})

result.index
result.columns
result[\'平均值\']

result2 = data.groupby(by=[\'class\', \'name\'])[[\'score\', \'score2\']].agg([
    numpy.size, 
    numpy.sum
])

result2.index
result2.columns
result2[\'score\']
result2[\'score\'][\'sum\']

result.reset_index()
result2.reset_index()

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分布分析：

import numpy;
import pandas;
from pandas import read_csv;

data = read_csv(\'D:\\\\PA\\\\8.3\\\\data.csv\');

bins = [min(data.年龄)-1, 20, 30, 40, max(data.年龄)+1];
labels = [\'20岁以及以下\', \'21岁到30岁\', \'31岁到40岁\', \'41岁以上\'];

年龄分层 = pandas.cut(data.年龄, bins, labels=labels)

data[\'年龄分层\'] = 年龄分层;

data.groupby(by=[\'年龄分层\'])[\'年龄\'].agg({\'人数\':numpy.size})

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交叉分析：

import numpy;
import pandas;
from pandas import read_csv;

df = read_csv(\'D:\\\\PA\\\\8.4\\\\data.csv\');

bins = [min(df.年龄)-1, 20, 30, 40, max(df.年龄)+1];
labels = [\'20岁以及以下\', \'21岁到30岁\', \'31岁到40岁\', \'41岁以上\'];

年龄分层 = pandas.cut(df.年龄, bins, labels=labels)
df[\'年龄分层\'] = 年龄分层;

r1 = df.pivot_table(
    values=[\'年龄\'], 
    index=[\'年龄分层\'], 
    columns=[\'性别\'], 
    aggfunc=[numpy.size, numpy.mean]
);

r2 = df.pivot_table(
    values=[\'年龄\'], 
    index=[\'年龄分层\'], 
    columns=[\'性别\'], 
    aggfunc=[numpy.std]
);

r1.join(r2)

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结构分析：

import numpy;
from pandas import read_csv;

data = read_csv(\'C:\\\\PA\\\\8.5\\\\data.csv\');

data_pt = data.pivot_table(
    values=[\'月消费（元）\'], 
    index=[\'省份\'], 
    columns=[\'通信品牌\'], 
    aggfunc=[numpy.sum]
);

data_pt.sum()

data_pt.sum(axis=0)

data_pt.sum(axis=1)

data_pt.div(data_pt.sum(axis=1), axis=0);

data_pt.div(data_pt.sum(axis=0), axis=1);

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相关分析：

# -*- coding: utf-8 -*-
from pandas import read_csv;
data = read_csv(\'D:\\\\PA\\\\8.6\\\\data.csv\');

#先来看看如何进行两个列之间的相关度的计算
data[\'人口\'].corr(data[\'文盲率\'])

#多列之间的相关度的计算方法
#选择多列的方法
#data.loc[:, [\'列1\', \'列2\', \'……\', \'列n\']]
data.loc[:, [\'超市购物率\', \'网上购物率\', \'文盲率\', \'人口\']].corr()

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