Python数据分析入门
2015-12-01 11:39
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Python数据分析入门
存储,学习,共享最近,Analysis with Programming加入了Planet
Python。作为该网站的首批特约博客,我这里来分享一下如何通过Python来开始数据分析。具体内容如下:
数据导入
导入本地的或者web端的CSV文件;
数据变换;
数据统计描述;
假设检验
单样本t检验;
可视化;
创建自定义函数。
数据导入
这是很关键的一步,为了后续的分析我们首先需要导入数据。通常来说,数据是CSV格式,就算不是,至少也可以转换成CSV格式。在Python中,我们的操作如下:Python
12345678 | import pandas as pd # Reading data locallydf = pd.read_csv('/Users/al-ahmadgaidasaad/Documents/d.csv') # Reading data from webdata_url = "https://raw.githubusercontent.com/alstat/Analysis-with-Programming/master/2014/Python/Numerical-Descriptions-of-the-Data/data.csv"df = pd.read_csv(data_url) |
数据变换
既然在工作空间有了数据,接下来就是数据变换。统计学家和科学家们通常会在这一步移除分析中的非必要数据。我们先看看数据:Python1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Head of the data df.head() # OUTPUT Abra Apayao Benguet Ifugao Kalinga 0 1243 2934 148 3300 10553 1 4158 9235 4287 8063 35257 2 1787 1922 1955 1074 4544 3 17152 14501 3536 19607 31687 4 1266 2385 2530 3315 8520 # Tail of the data df.tail() # OUTPUT Abra Apayao Benguet Ifugao Kalinga 74 2505 20878 3519 19737 16513 75 60303 40065 7062 19422 61808 76 6311 6756 3561 15910 23349 77 13345 38902 2583 11096 68663 78 2623 18264 3745 16787 16900 |
在R语言中,数据列和行的名字通过colnames和rownames来分别进行提取。在Python中,我们则使用columns和index属性来提取,如下:
Python
1234567891011 | # Extracting column namesprint df.columns # OUTPUTIndex([u'Abra', u'Apayao', u'Benguet', u'Ifugao', u'Kalinga'], dtype='object') # Extracting row names or the indexprint df.index # OUTPUTInt64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78], dtype='int64') |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | # Transpose data df.T # OUTPUT 0 1 2 3 4 5 6 7 8 9 Abra 1243 4158 1787 17152 1266 5576 927 21540 1039 5424 Apayao 2934 9235 1922 14501 2385 7452 1099 17038 1382 10588 Benguet 148 4287 1955 3536 2530 771 2796 2463 2592 1064 Ifugao 3300 8063 1074 19607 3315 13134 5134 14226 6842 13828 Kalinga 10553 35257 4544 31687 8520 28252 3106 36238 4973 40140 ... 69 70 71 72 73 74 75 76 77 Abra ... 12763 2470 59094 6209 13316 2505 60303 6311 13345 Apayao ... 37625 19532 35126 6335 38613 20878 40065 6756 38902 Benguet ... 2354 4045 5987 3530 2585 3519 7062 3561 2583 Ifugao ... 9838 17125 18940 15560 7746 19737 19422 15910 11096 Kalinga ... 65782 15279 52437 24385 66148 16513 61808 23349 68663 78 Abra 2623 Apayao 18264 Benguet 3745 Ifugao 16787 Kalinga 16900 Other transformations such as sort can be done using <code>sort</code> attribute. Now let's extract a specific column. In Python, we do it using either <code>iloc</code> or <code>ix</code> attributes, but <code>ix</code> is more robust and thus I prefer it. Assuming we want the head of the first column of the data, we have |
Python
123456789 | print df.ix[:, 0].head() # OUTPUT0 12431 41582 17873 171524 1266Name: Abra, dtype: int64 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | print df.ix[10:20, 0:3] # OUTPUT Abra Apayao Benguet 10 981 1311 2560 11 27366 15093 3039 12 1100 1701 2382 13 7212 11001 1088 14 1048 1427 2847 15 25679 15661 2942 16 1055 2191 2119 17 5437 6461 734 18 1029 1183 2302 19 23710 12222 2598 20 1091 2343 2654 |
df.ix[10:20, ['Abra', 'Apayao', 'Benguet']]。
为了舍弃数据中的列,这里是列1(Apayao)和列2(Benguet),我们使用drop属性,如下:
Python
123456789 | print df.drop(df.columns[[1, 2]], axis = 1).head() # OUTPUT Abra Ifugao Kalinga0 1243 3300 105531 4158 8063 352572 1787 1074 45443 17152 19607 316874 1266 3315 8520 |
axis参数告诉函数到底舍弃列还是行。如果
axis等于0,那么就舍弃行。
统计描述
下一步就是通过describe属性,对数据的统计特性进行描述:Python
1 2 3 4 5 6 7 8 9 10 11 12 | print df.describe() # OUTPUT Abra Apayao Benguet Ifugao Kalinga count 79.000000 79.000000 79.000000 79.000000 79.000000 mean 12874.379747 16860.645570 3237.392405 12414.620253 30446.417722 std 16746.466945 15448.153794 1588.536429 5034.282019 22245.707692 min 927.000000 401.000000 148.000000 1074.000000 2346.000000 25% 1524.000000 3435.500000 2328.000000 8205.000000 8601.500000 50% 5790.000000 10588.000000 3202.000000 13044.000000 24494.000000 75% 13330.500000 33289.000000 3918.500000 16099.500000 52510.500000 max 60303.000000 54625.000000 8813.000000 21031.000000 68663.000000 |
假设检验
Python有一个很好的统计推断包。那就是scipy里面的stats。ttest_1samp实现了单样本t检验。因此,如果我们想检验数据Abra列的稻谷产量均值,通过零假设,这里我们假定总体稻谷产量均值为15000,我们有:Python
1234567 | from scipy import stats as ss # Perform one sample t-test using 1500 as the true meanprint ss.ttest_1samp(a = df.ix[:, 'Abra'], popmean = 15000) # OUTPUT(-1.1281738488299586, 0.26270472069109496) |
t统计量
prob : 浮点或数组类型
two-tailed p-value 双侧概率值
通过上面的输出,看到p值是0.267远大于α等于0.05,因此没有充分的证据说平均稻谷产量不是150000。将这个检验应用到所有的变量,同样假设均值为15000,我们有:Python
1 2 3 4 5 6 | print ss.ttest_1samp(a = df, popmean = 15000) # OUTPUT (array([ -1.12817385, 1.07053437, -65.81425599, -4.564575 , 6.17156198]), array([ 2.62704721e-01, 2.87680340e-01, 4.15643528e-70, 1.83764399e-05, 2.82461897e-08])) |
可视化
Python中有许多可视化模块,最流行的当属matpalotlib库。稍加提及,我们也可选择bokeh和seaborn模块。之前的博文中,我已经说明了matplotlib库中的盒须图模块功能。![](http://ww2.sinaimg.cn/mw690/6941baebgw1epzcsbms47j20hr0hk0uk.jpg)
Python
123 | # Import the module for plottingimport matplotlib.pyplot as plt plt.show(df.plot(kind = 'box')) |
1 2 3 | import matplotlib.pyplot as plt pd.options.display.mpl_style = 'default' # Sets the plotting display theme to ggplot2 df.plot(kind = 'box') |
![](http://ww3.sinaimg.cn/mw690/6941baebgw1epzcsb7dx5j20hi0hedhe.jpg)
比matplotlib.pyplot主题简洁太多。但是在本博文中,我更愿意引入seaborn模块,该模块是一个统计数据可视化库。因此我们有:
![](http://ww3.sinaimg.cn/mw690/6941baebgw1epzcsatuw7j20fc0fajrz.jpg)
Python
1234 | # Import the seaborn libraryimport seaborn as sns # Do the boxplotplt.show(sns.boxplot(df, widths = 0.5, color = "pastel")) |
![](http://ww4.sinaimg.cn/mw690/6941baebgw1epzcsaf9w9j20fk0fagmf.jpg)
Python
1 | plt.show(sns.violinplot(df, widths = 0.5, color = "pastel")) |
![](http://ww1.sinaimg.cn/mw690/6941baebgw1epzcsa9lalj20g60fvgmo.jpg)
Python
1 | plt.show(sns.distplot(df.ix[:,2], rug = True, bins = 15)) |
![](http://ww4.sinaimg.cn/mw690/6941baebgw1epzcs9mmfdj20hb0gut9n.jpg)
Python
1 2 | with sns.axes_style("white"): plt.show(sns.jointplot(df.ix[:,1], df.ix[:,2], kind = "kde")) |
![](http://ww3.sinaimg.cn/mw690/6941baebgw1epzcs96mw3j20g40geq41.jpg)
Python
1 | plt.show(sns.lmplot("Benguet", "Ifugao", df)) |
创建自定义函数
在Python中,我们使用def函数来实现一个自定义函数。例如,如果我们要定义一个两数相加的函数,如下即可:Python1 2 3 4 5 6 7 | def add_2int(x, y): return x + y add_2int(2, 2) # OUTPUT 4 |
产生10个正态分布样本,其中
![](http://jbcdn2.b0.upaiyun.com/2015/02/956570501167bbd18b366a0a355c51f1.jpg)
和
![](http://jbcdn2.b0.upaiyun.com/2015/02/d282fda7e65136a4799422daa92ab4b7.jpg)
基于95%的置信度,计算
![](http://jbcdn2.b0.upaiyun.com/2015/02/ad658d3ce3077722d3581e2a5cc5aff7.jpg)
和
![](http://jbcdn2.b0.upaiyun.com/2015/02/e5d6692f794081ca0de17fece7f3b78c.jpg)
;
重复100次; 然后
计算出置信区间包含真实均值的百分比
Python中,程序如下:
Python
12345678910111213141516171819202122232425 | import numpy as npimport scipy.stats as ss def case(n = 10, mu = 3, sigma = np.sqrt(5), p = 0.025, rep = 100): m = np.zeros((rep, 4)) for i in range(rep): norm = np.random.normal(loc = mu, scale = sigma, size = n) xbar = np.mean(norm) low = xbar - ss.norm.ppf(q = 1 - p) * (sigma / np.sqrt(n)) up = xbar + ss.norm.ppf(q = 1 - p) * (sigma / np.sqrt(n)) if (mu > low) & (mu < up): rem = 1 else: rem = 0 m[i, :] = [xbar, low, up, rem] inside = np.sum(m[:, 3]) per = inside / rep desc = "There are " + str(inside) + " confidence intervals that contain " "the true mean (" + str(mu) + "), that is " + str(per) + " percent of the total CIs" return {"Matrix": m, "Decision": desc} |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | import numpy as np import scipy.stats as ss def case2(n = 10, mu = 3, sigma = np.sqrt(5), p = 0.025, rep = 100): scaled_crit = ss.norm.ppf(q = 1 - p) * (sigma / np.sqrt(n)) norm = np.random.normal(loc = mu, scale = sigma, size = (rep, n)) xbar = norm.mean(1) low = xbar - scaled_crit up = xbar + scaled_crit rem = (mu > low) & (mu < up) m = np.c_[xbar, low, up, rem] inside = np.sum(m[:, 3]) per = inside / rep desc = "There are " + str(inside) + " confidence intervals that contain " "the true mean (" + str(mu) + "), that is " + str(per) + " percent of the total CIs" return {"Matrix": m, "Decision": desc} |
更新
那些对于本文ipython notebook版本感兴趣的,请点击这里。这篇文章由NuttensClaude负责转换成 ipython notebook 。
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