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:

import pandas as pd# Reading data locally
df = pd.read_csv(‘/Users/al-ahmadgaidasaad/Documents/d.csv’)# Reading data from web
data_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)
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)

为了读取本地CSV文件,我们需要pandas这个数据分析库中的相应模块。其中的read_csv函数能够读取本地和web数据。

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:

# Head of the data
print df.head()# OUTPUT
AbraApayaoBenguetIfugaoKalinga
0 12432934148330010553
1 41589235 4287806335257
2 17871922 19551074 4544
317152 14501 3536 1960731687
4 12662385 25303315 8520# Tail of the data
print df.tail()# OUTPUT
AbraApayaoBenguetIfugaoKalinga
74 2505 20878 3519 1973716513
7560303 40065 7062 1942261808
76 63116756 3561 1591023349
7713345 38902 2583 1109668663
78 2623 18264 3745 1678716900
# Head of the dataprint df.head()# OUTPUTAbraApayaoBenguetIfugaoKalinga0 124329341483300105531 41589235 42878063352572 17871922 19551074 4544317152 14501 3536 19607316874 12662385 25303315 8520# Tail of the dataprint df.tail()# OUTPUT AbraApayaoBenguetIfugaoKalinga74 2505 20878 3519 19737165137560303 40065 7062 194226180876 63116756 3561 15910233497713345 38902 2583 110966866378 2623 18264 3745 1678716900

对R语言程序员来说,上述操作等价于通过print(head(df))来打印数据的前6行,以及通过print(tail(df))来打印数据的后6行。当然Python中,默认打印是5行,而R则是6行。因此R的代码head(df, n = 10),在Python中就是df.head(n = 10),打印数据尾部也是同样道理。

 

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:

# Extracting column names
print df.columns# OUTPUT
Index([u’Abra’, u’Apayao’, u’Benguet’, u’Ifugao’, u’Kalinga’], dtype=’object’)# Extracting row names or the index
print df.index# OUTPUT
Int64Index([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′)
# 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′)

数据转置使用T方法,

# Transpose data
print df.T# OUTPUT
01 23 45 67 89
Abra1243 41581787171521266 5576 927215401039 5424
Apayao2934 92351922145012385 7452109917038138210588
Benguet148 42871955 353625307712796 24632592 1064
Ifugao3300 8063107419607331513134513414226684213828
Kalinga1055335257454431687852028252310636238497340140 … 69 70 71 72 73 74 75 76 77
Abra …12763 247059094 620913316 250560303 631113345
Apayao …376251953235126 6335386132087840065 675638902
Benguet… 2354 4045 5987 3530 2585 3519 7062 3561 2583
Ifugao … 9838171251894015560 774619737194221591011096
Kalinga…657821527952437243856614816513618082334968663 78
Abra2623
Apayao 18264
Benguet 3745
Ifugao 16787
Kalinga16900Other 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
# Transpose dataprint df.T# OUTPUT01 23 45 67 89 Abra1243 41581787171521266 5576 927215401039 5424 Apayao2934 92351922145012385 7452109917038138210588 Benguet148 42871955 353625307712796 24632592 1064 Ifugao3300 8063107419607331513134513414226684213828 Kalinga1055335257454431687852028252310636238497340140… 69 70 71 72 73 74 75 76 77Abra …12763 247059094 620913316 250560303 631113345 Apayao …376251953235126 6335386132087840065 675638902 Benguet… 2354 4045 5987 3530 2585 3519 7062 3561 2583 Ifugao … 9838171251894015560 774619737194221591011096 Kalinga…657821527952437243856614816513618082334968663 78Abra2623Apayao 18264Benguet 3745Ifugao 16787Kalinga16900Other 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

其他变换,例如排序就是用sort属性。现在我们提取特定的某列数据。Python中,可以使用iloc或者ix属性。但是我更喜欢用ix,因为它更稳定一些。假设我们需数据第一列的前5行,我们有:

print df.ix[:, 0].head()# OUTPUT
0 1243
1 4158
2 1787
317152
4 1266
Name: Abra, dtype: int64
print df.ix[:, 0].head()# OUTPUT0 12431 41582 17873171524 1266Name: Abra, dtype: int64

顺便提一下,Python的索引是从0开始而非1。为了取出从11到20行的前3列数据,我们有:

print df.ix[10:20, 0:3]# OUTPUT
AbraApayaoBenguet
109811311 2560
1127366 15093 3039
12 11001701 2382
13 7212 11001 1088
14 10481427 2847
1525679 15661 2942
16 10552191 2119
17 54376461734
18 10291183 2302
1923710 12222 2598
20 10912343 2654
print df.ix[10:20, 0:3]# OUTPUTAbraApayaoBenguet109811311 25601127366 15093 303912 11001701 238213 7212 11001 108814 10481427 28471525679 15661 294216 10552191 211917 5437646173418 10291183 23021923710 12222 259820 10912343 2654

上述命令相当于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:

print df.drop(df.columns[[1, 2]], axis = 1).head()# OUTPUT
AbraIfugaoKalinga
0 1243330010553
1 4158806335257
2 17871074 4544
317152 1960731687
4 12663315 8520
print df.drop(df.columns[[1, 2]], axis = 1).head()# OUTPUTAbraIfugaoKalinga0 12433300105531 41588063352572 17871074 4544317152 19607316874 12663315 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:

print df.describe()# OUTPUT
AbraApayaoBenguetIfugao Kalinga
count 79.000000 79.00000079.000000 79.000000 79.000000
mean 12874.37974716860.6455703237.39240512414.62025330446.417722
std16746.46694515448.1537941588.536429 5034.28201922245.707692
min927.000000401.000000 148.000000 1074.000000 2346.000000
25% 1524.000000 3435.5000002328.000000 8205.000000 8601.500000
50% 5790.00000010588.0000003202.00000013044.00000024494.000000
75%13330.50000033289.0000003918.50000016099.50000052510.500000
max60303.00000054625.0000008813.00000021031.00000068663.000000
print df.describe()# OUTPUT AbraApayaoBenguetIfugao Kalingacount 79.000000 79.00000079.000000 79.000000 79.000000mean 12874.37974716860.6455703237.39240512414.62025330446.417722std16746.46694515448.1537941588.536429 5034.28201922245.707692min927.000000401.000000 148.000000 1074.000000 2346.00000025% 1524.000000 3435.5000002328.000000 8205.000000 8601.50000050% 5790.00000010588.0000003202.00000013044.00000024494.00000075%13330.50000033289.0000003918.50000016099.50000052510.500000max60303.00000054625.0000008813.00000021031.00000068663.000000
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:

from scipy import stats as ss# Perform one sample t-test using 1500 as the true mean
print ss.ttest_1samp(a = df.ix[:, ‘Abra’], popmean = 15000)# OUTPUT
(-1.1281738488299586, 0.26270472069109496)
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)

返回下述值组成的元祖:

t : 浮点或数组类型

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:

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]))
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]))

第一个数组是t统计量,第二个数组则是相应的p值。

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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。

 

Python数据分析入门_Python

# Import the module for plotting
import matplotlib.pyplot as plt
plt.show(df.plot(kind = ‘box’))
# Import the module for plottingimport matplotlib.pyplot as plt plt.show(df.plot(kind = ‘box’))

现在,我们可以用pandas模块中集成R的ggplot主题来美化图表。要使用ggplot,我们只需要在上述代码中多加一行,

import matplotlib.pyplot as plt
pd.options.display.mpl_style = ‘default’ # Sets the plotting display theme to ggplot2
df.plot(kind = ‘box’)
import matplotlib.pyplot as pltpd.options.display.mpl_style = ‘default’ # Sets the plotting display theme to ggplot2df.plot(kind = ‘box’)

这样我们就得到如下图表:

 

Python数据分析入门_Python

 

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:

 

Python数据分析入门_Python

# Import the seaborn library
import seaborn as sns
# Do the boxplot
plt.show(sns.boxplot(df, widths = 0.5, color = “pastel”))
# Import the seaborn libraryimport seaborn as sns # Do the boxplotplt.show(sns.boxplot(df, widths = 0.5, color = “pastel”))

多性感的盒式图,继续往下看。

Python数据分析入门_Python

plt.show(sns.violinplot(df, widths = 0.5, color = “pastel”))
plt.show(sns.violinplot(df, widths = 0.5, color = “pastel”))

Python数据分析入门_Python

plt.show(sns.distplot(df.ix[:,2], rug = True, bins = 15))
plt.show(sns.distplot(df.ix[:,2], rug = True, bins = 15))

Python数据分析入门_Python

with sns.axes_style(“white”):
plt.show(sns.jointplot(df.ix[:,1], df.ix[:,2], kind = “kde”))
with sns.axes_style(“white”):plt.show(sns.jointplot(df.ix[:,1], df.ix[:,2], kind = “kde”))

Python数据分析入门_Python

plt.show(sns.lmplot(“Benguet”, “Ifugao”, df))
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:

def add_2int(x, y):
return x + yprint add_2int(2, 2)# OUTPUT
4
def add_2int(x, y):return x + yprint add_2int(2, 2)# OUTPUT4

顺便说一下,Python中的缩进是很重要的。通过缩进来定义函数作用域,就像在R语言中使用大括号{…}一样。这有一个我们之前博文的例子:

产生10个正态分布样本,其中 Python数据分析入门_PythonPython数据分析入门_Python
基于95%的置信度,计算 Python数据分析入门_PythonPython数据分析入门_Python ;
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:

import numpy as np
import scipy.stats as ssdef 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 = 0m[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}
import numpy as npimport scipy.stats as ssdef 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 = 1else:rem = 0m[i, :] = [xbar, low, up, rem]inside = np.sum(m[:, 3])per = inside / repdesc = “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}

上述代码读起来很简单,但是循环的时候就很慢了。下面针对上述代码进行了改进,这多亏了 Python专家,看我上篇博文的15条意见吧。

import numpy as np
import scipy.stats as ssdef 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_critrem = (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}
import numpy as npimport scipy.stats as ssdef 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_critup = xbar + scaled_critrem = (mu > low) &; (mu < up)m = np.c_[xbar, low, up, rem]inside = np.sum(m[:, 3])per = inside / repdesc = “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Nuttens Claude负责转换成 ipython notebook 。