python数据可视化读取excell文件绘制图像详细教程

python数据可视化读取excell文件绘制图像详细教程
python数据可视化
python库中的基本用法

import pandas as pd  # 调用pandas库 来读取excell的文件
import scipy.stats as ss  #生成正太分布的库
import numpy as np
import seaborn as sns
df=pd.read_csv("./data/qb_data.csv")  # 读取excell的文件**
print(df)print(df.head(10))  # 得到数据的前十行print(type(df))  #求读取的数据的类型print(type(df["Name"])) # 求每列行的值的类型print(df.mean())  # 求均值
print(df["Age"].mean())  #求指定的每一列的均值print(df["Cmp"].median)   # 求指定的这一列的中位数print("求Att的分位数是：",df["Att"].quantile(q=0.5)) # 求分位数print(df.quantile(q=0.5)) # 求分位数
print(df.mode())print("Age的众数:",df["Age"].mode()) #求众数print("Age的标准差:",df["Age"].std()) # 求标准差
print("Age的方差:",df["Age"].var()) #求方差print("Age的和:",df["Age"].sum())   #求和print("Age的偏态系数:",df["Age"].skew())   #求偏态系数print("Age的和:",df["Age"].kurt())   #求峰态系数print(ss.norm)print("Age的正态分布:",ss.norm.stats(moments="mvsk"))   #求正态分布print(ss.norm.pdf(0.0)) #分布函数在0上面的值print(ss.norm.ppf(0.9))   #累计值print(ss.norm.cdf(2))  #从负无穷到2的累计概率是多少print(ss.norm.cdf(2)-ss.norm.cdf(-2))print(ss.norm.rvs(size=10))  # 10个符合正态分布的数字
print(df["Age"].sample(10)) # 对其中的一列进行十个抽样
sns.set_style(style="ticks")
sns.set_context(context="poster",font_scale=0.5)
sns.countplot(x="Age",data=df)  # 直接调用seaborn库绘制柱状图
sl_s=df["TD"]
print(sl_s.isnull())  #判断是否有异常值
print(sl_s[sl_s.isnull()])
print(df[df["TD"].isnull()])
sl_s=sl_s.dropna()
print(sl_s.min())
print(sl_s.median())
print(sl_s.quantile(q=0.25))
print(sl_s.quantile(q=0.75))
import seaborn as sns
import matplotlib.pyplot as plt
plt.title("Age")
plt.xlabel("age")
plt.ylabel("Number")
plt.bar(np.arange(len(df["Age"].value_counts()))+0.5,df["Age"].value_counts())
plt.xticks(np.arange(len(df["Age"].value_counts()))+0.5,df["Age"].value_counts().index)
for x,y in zip(np.arange(len(df["Age"].value_counts()))+0.5,df["Age"].value_counts()):plt.text(x,y,y,ha="center",va="bottom")
plt.savefig('Age.png')  #保存图片
plt.show()
print("这是Cmp")
plt.title("Cmp")
plt.xlabel("cmp")
plt.ylabel("Number")
plt.xticks(np.arange(len(df["Cmp"].value_counts()))+0.5,df["Cmp"].value_counts().index)
plt.bar(np.arange(len(df["Cmp"].value_counts()))+0.5,df["Cmp"].value_counts(),width=0.5)
plt.axis([0,20,0,40])
for x,y in zip(np.arange(len(df["Cmp"].value_counts()))+0.5,df["Cmp"].value_counts()):plt.text(x,y,y,ha="center",va="bottom")
plt.savefig('Cmp.png')  #保存图片
plt.show()
print("这是TD")
plt.title("TD")
plt.xlabel("td")
plt.ylabel("Number")
# sns.set_style(style="whitegrid")
# sns.set_context(context="postor",font_scale=0.05)
plt.xticks(np.arange(len(df["TD"].value_counts()))+0.5,df["TD"].value_counts().index)
# plt.axis([0,20,0,30])
plt.bar(np.arange(len(df["TD"].value_counts()))+0.5,df["TD"].value_counts(),width=0.5)
for x,y in zip(np.arange(len(df["TD"].value_counts()))+0.5,df["TD"].value_counts()):plt.text(x,y,y,ha="center",va="bottom")
plt.savefig('TD.png')  #保存图片
plt.show()
print("——————————————————"*20)
print(np.histogram(sl_s.values,bins=np.arange(20,50,100)))

#文件的架构图：

qb_data.csv的数据如下:

Name,Age,Year,Cmp,Att,Yds,TD
Peyton Manning,38,1998,326,575,3739,26
Peyton Manning,38,1999,331,533,4135,26
Peyton Manning,38,2000,357,571,4413,33
Peyton Manning,38,2001,343,547,4131,26
Peyton Manning,38,2002,392,591,4200,27
Peyton Manning,38,2003,379,566,4267,29
Peyton Manning,38,2004,336,497,4557,49
Peyton Manning,38,2005,305,453,3747,28
Peyton Manning,38,2006,362,557,4397,31
Peyton Manning,38,2007,337,515,4040,31
Peyton Manning,38,2008,371,555,4002,27
Peyton Manning,38,2009,393,571,4500,33
Peyton Manning,38,2010,450,679,4700,33
Peyton Manning,38,2011,0,0,0,0
Peyton Manning,38,2012,400,583,4659,37
Peyton Manning,38,2013,450,659,5477,55
Peyton Manning,38,2014,395,597,4727,39
Peyton Manning,38,2014,5927,9049,69691,530
Brett Favre,41,1991,0,4,0,0
Brett Favre,41,1992,302,471,3227,18
Brett Favre,41,1993,318,522,3303,19
Brett Favre,41,1994,363,582,3882,33
Brett Favre,41,1995,359,570,4413,38
Brett Favre,41,1996,325,543,3899,39
Brett Favre,41,1997,304,513,3867,35
Brett Favre,41,1998,347,551,4212,31
Brett Favre,41,1999,341,595,4091,22
Brett Favre,41,2000,338,580,3812,20
Brett Favre,41,2001,314,510,3921,32
Brett Favre,41,2002,341,551,3658,27
Brett Favre,41,2003,308,471,3361,32
Brett Favre,41,2004,346,540,4088,30
Brett Favre,41,2005,372,607,3881,20
Brett Favre,41,2006,343,613,3885,18
Brett Favre,41,2007,356,535,4155,28
Brett Favre,41,2008,343,522,3472,22
Brett Favre,41,2009,363,531,4202,33
Brett Favre,41,2010,217,358,2509,11
Brett Favre,41,2010,6300,10169,71838,508
Dan Marino,38,1983,173,296,2210,20
Dan Marino,38,1984,362,564,5084,48
Dan Marino,38,1985,336,567,4137,30
Dan Marino,38,1986,378,623,4746,44
Dan Marino,38,1987,263,444,3245,26
Dan Marino,38,1988,354,606,4434,28
Dan Marino,38,1989,308,550,3997,24
Dan Marino,38,1990,306,531,3563,21
Dan Marino,38,1991,318,549,3970,25
Dan Marino,38,1992,330,554,4116,24
Dan Marino,38,1993,91,150,1218,8
Dan Marino,38,1994,385,615,4453,30
Dan Marino,38,1995,309,482,3668,24
Dan Marino,38,1996,221,373,2795,17
Dan Marino,38,1997,319,548,3780,16
Dan Marino,38,1998,310,537,3497,23
Dan Marino,38,1999,204,369,2448,12
Dan Marino,38,2010,4967,8358,61361,420
Drew Brees,35,2001,15,27,221,1
Drew Brees,35,2002,320,526,3284,17
Drew Brees,35,2003,205,356,2108,11
Drew Brees,35,2004,262,400,3159,27
Drew Brees,35,2005,323,500,3576,24
Drew Brees,35,2006,356,554,4418,26
Drew Brees,35,2007,440,652,4423,28
Drew Brees,35,2008,413,635,5069,34
Drew Brees,35,2009,363,514,4388,34
Drew Brees,35,2010,448,658,4620,33
Drew Brees,35,2011,468,657,5476,46
Drew Brees,35,2012,422,670,5177,43
Drew Brees,35,2013,446,650,5162,39
Drew Brees,35,2014,456,659,4952,33
Drew Brees,35,2010,4937,7458,56033,396
Tom Brady,37,2000,1,3,6,0
Tom Brady,37,2001,264,413,2843,18
Tom Brady,37,2002,373,601,3764,28
Tom Brady,37,2003,317,527,3620,23
Tom Brady,37,2004,288,474,3692,28
Tom Brady,37,2005,334,530,4110,26
Tom Brady,37,2006,319,516,3529,24
Tom Brady,37,2007,398,578,4806,50
Tom Brady,37,2008,7,11,76,0
Tom Brady,37,2009,371,565,4398,28
Tom Brady,37,2010,324,492,3900,36
Tom Brady,37,2011,401,611,5235,39
Tom Brady,37,2012,401,637,4827,34
Tom Brady,37,2013,380,628,4343,25
Tom Brady,37,2014,373,582,4109,33
Tom Brady,37,2010,4551,7168,53258,392
Fran Tarkenton,38,1961,157,280,1997,18
Fran Tarkenton,38,1962,163,329,2595,22
Fran Tarkenton,38,1963,170,297,2311,15
Fran Tarkenton,38,1964,171,306,2506,22
Fran Tarkenton,38,1965,171,329,2609,19
Fran Tarkenton,38,1966,192,358,2561,17
Fran Tarkenton,38,1967,204,377,3088,29
Fran Tarkenton,38,1968,182,337,2555,21
Fran Tarkenton,38,1969,220,409,2918,23
Fran Tarkenton,38,1970,219,389,2777,19
Fran Tarkenton,38,1971,226,386,2567,11
Fran Tarkenton,38,1972,215,378,2651,18
Fran Tarkenton,38,1973,169,274,2113,15
Fran Tarkenton,38,1974,199,351,2598,17
Fran Tarkenton,38,1975,273,425,2994,25
Fran Tarkenton,38,1976,255,412,2961,17
Fran Tarkenton,38,1977,155,258,1734,9
Fran Tarkenton,38,1978,345,572,3468,25
Fran Tarkenton,38,2010,3686,6467,47003,342
John Elway,38,1983,123,259,1663,7
John Elway,38,1984,214,380,2598,18
John Elway,38,1985,327,605,3891,22
John Elway,38,1986,280,504,3485,19
John Elway,38,1987,224,410,3198,19
John Elway,38,1988,274,496,3309,17
John Elway,38,1989,223,416,3051,18
John Elway,38,1990,294,502,3526,15
John Elway,38,1991,242,451,3253,13
John Elway,38,1992,174,316,2242,10
John Elway,38,1993,348,551,4030,25
John Elway,38,1994,307,494,3490,16
John Elway,38,1995,316,542,3970,26
John Elway,38,1996,287,466,3328,26
John Elway,38,1997,280,502,3635,27
John Elway,38,1998,210,356,2806,22
John Elway,38,2010,4123,7250,51475,300
Warren Moon,44,1984,259,450,3338,12
Warren Moon,44,1985,200,377,2709,15
Warren Moon,44,1986,256,488,3489,13
Warren Moon,44,1987,184,368,2806,21
Warren Moon,44,1988,160,294,2327,17
Warren Moon,44,1989,280,464,3631,23
Warren Moon,44,1990,362,584,4689,33
Warren Moon,44,1991,404,655,4690,23
Warren Moon,44,1992,224,346,2521,18
Warren Moon,44,1993,303,520,3485,21
Warren Moon,44,1994,371,601,4264,18
Warren Moon,44,1995,377,606,4228,33
Warren Moon,44,1996,134,247,1610,7
Warren Moon,44,1997,313,528,3678,25
Warren Moon,44,1998,145,258,1632,11
Warren Moon,44,1999,1,3,20,0
Warren Moon,44,2000,15,34,208,1
Warren Moon,44,2010,3988,6823,49325,291
Johny Unitas,40,1956,110,198,1498,9
Johny Unitas,40,1957,172,301,2550,24
Johny Unitas,40,1958,136,263,2007,19
Johny Unitas,40,1959,193,367,2899,32
Johny Unitas,40,1960,190,378,3099,25
Johny Unitas,40,1961,229,420,2990,16
Johny Unitas,40,1962,222,389,2967,23
Johny Unitas,40,1963,237,410,3481,20
Johny Unitas,40,1964,158,305,2824,19
Johny Unitas,40,1965,164,282,2530,23
Johny Unitas,40,1966,195,348,2748,22
Johny Unitas,40,1967,255,436,3428,20
Johny Unitas,40,1968,11,32,139,2
Johny Unitas,40,1969,178,327,2342,12
Johny Unitas,40,1970,166,321,2213,14
Johny Unitas,40,1971,92,176,942,3
Johny Unitas,40,1972,88,157,1111,4
Johny Unitas,40,1973,34,76,471,3
Johny Unitas,40,2010,2830,5186,40239,290
Vinny Testaverde,44,1987,71,165,1081,5
Vinny Testaverde,44,1988,222,466,3240,13
Vinny Testaverde,44,1989,258,480,3133,20
Vinny Testaverde,44,1990,203,365,2818,17
Vinny Testaverde,44,1991,166,326,1994,8
Vinny Testaverde,44,1992,206,358,2554,14
Vinny Testaverde,44,1993,130,230,1797,14
Vinny Testaverde,44,1994,207,376,2575,16
Vinny Testaverde,44,1995,241,392,2883,17
Vinny Testaverde,44,1996,325,549,4177,33
Vinny Testaverde,44,1997,271,470,2971,18
Vinny Testaverde,44,1998,259,421,3256,29
Vinny Testaverde,44,1999,10,15,96,1
Vinny Testaverde,44,2000,328,590,3732,21
Vinny Testaverde,44,2001,260,441,2752,15
Vinny Testaverde,44,2002,54,83,499,3
Vinny Testaverde,44,2003,123,198,1385,7
Vinny Testaverde,44,2004,297,495,3532,17
Vinny Testaverde,44,2005,60,106,777,1
Vinny Testaverde,44,2006,2,3,29,1
Vinny Testaverde,44,2007,94,172,952,5
Vinny Testaverde,44,2010,3787,6701,46233,275
Joe Montana,38,1979,13,23,96,1
Joe Montana,38,1980,176,273,1795,15
Joe Montana,38,1981,311,488,3565,19
Joe Montana,38,1982,213,346,2613,17
Joe Montana,38,1983,332,515,3910,26
Joe Montana,38,1984,279,432,3630,28
Joe Montana,38,1985,303,494,3653,27
Joe Montana,38,1986,191,307,2236,8
Joe Montana,38,1987,266,398,3054,31
Joe Montana,38,1988,238,397,2981,18
Joe Montana,38,1989,271,386,3521,26
Joe Montana,38,1990,321,520,3944,26
Joe Montana,38,1992,15,21,126,2
Joe Montana,38,1993,181,298,2144,13
Joe Montana,38,1994,299,493,3283,16
Joe Montana,38,2010,3409,5391,40551,273
Dave Krieg,40,1980,0,2,0,0
Dave Krieg,40,1981,64,112,843,7
Dave Krieg,40,1982,49,78,501,2
Dave Krieg,40,1983,147,243,2139,18
Dave Krieg,40,1984,276,480,3671,32
Dave Krieg,40,1985,285,532,3602,27
Dave Krieg,40,1986,225,375,2921,21
Dave Krieg,40,1987,178,294,2131,23
Dave Krieg,40,1988,134,228,1741,18
Dave Krieg,40,1989,286,499,3309,21
Dave Krieg,40,1990,265,448,3194,15
Dave Krieg,40,1991,187,285,2080,11
Dave Krieg,40,1992,230,413,3115,15
Dave Krieg,40,1993,105,189,1238,7
Dave Krieg,40,1994,131,212,1629,14
Dave Krieg,40,1995,304,521,3554,16
Dave Krieg,40,1996,226,377,2278,14
Dave Krieg,40,1997,1,2,2,0
Dave Krieg,40,1998,12,21,199,0
Dave Krieg,40,2010,3105,5311,38147,261
Eli Manning,33,2004,95,197,1043,6
Eli Manning,33,2005,294,557,3762,24
Eli Manning,33,2006,301,522,3244,24
Eli Manning,33,2007,297,529,3336,23
Eli Manning,33,2008,289,479,3238,21
Eli Manning,33,2009,317,509,4021,27
Eli Manning,33,2010,339,539,4002,31
Eli Manning,33,2011,359,589,4933,29
Eli Manning,33,2012,321,536,3948,26
Eli Manning,33,2013,317,551,3818,18
Eli Manning,33,2014,379,601,4410,30
Eli Manning,33,2010,3308,5609,39755,259
Sonny Jurgensen,40,1957,33,70,470,5
Sonny Jurgensen,40,1958,12,22,259,0
Sonny Jurgensen,40,1959,3,5,27,1
Sonny Jurgensen,40,1960,24,44,486,5
Sonny Jurgensen,40,1961,235,416,3723,32
Sonny Jurgensen,40,1962,196,366,3261,22
Sonny Jurgensen,40,1963,99,184,1413,11
Sonny Jurgensen,40,1964,207,385,2934,24
Sonny Jurgensen,40,1965,190,356,2367,15
Sonny Jurgensen,40,1966,254,436,3209,28
Sonny Jurgensen,40,1967,288,508,3747,31
Sonny Jurgensen,40,1968,167,292,1980,17
Sonny Jurgensen,40,1969,274,442,3102,22
Sonny Jurgensen,40,1970,202,337,2354,23
Sonny Jurgensen,40,1971,16,28,170,0
Sonny Jurgensen,40,1972,39,59,633,2
Sonny Jurgensen,40,1973,87,145,904,6
Sonny Jurgensen,40,1974,107,167,1185,11
Sonny Jurgensen,40,2010,2433,4262,32224,255
Dan Fouts,36,1973,87,194,1126,6
Dan Fouts,36,1974,115,237,1732,8
Dan Fouts,36,1975,106,195,1396,2
Dan Fouts,36,1976,208,359,2535,14
Dan Fouts,36,1977,69,109,869,4
Dan Fouts,36,1978,224,381,2999,24
Dan Fouts,36,1979,332,530,4082,24
Dan Fouts,36,1980,348,589,4715,30
Dan Fouts,36,1981,360,609,4802,33
Dan Fouts,36,1982,204,330,2883,17
Dan Fouts,36,1983,215,340,2975,20
Dan Fouts,36,1984,317,507,3740,19
Dan Fouts,36,1985,254,430,3638,27
Dan Fouts,36,1986,252,430,3031,16
Dan Fouts,36,1987,206,364,2517,10
Dan Fouts,36,2010,3297,5604,43040,254
Philip Rivers,33,2004,5,8,33,1
Philip Rivers,33,2005,12,22,115,0
Philip Rivers,33,2006,284,460,3388,22
Philip Rivers,33,2007,277,460,3152,21
Philip Rivers,33,2008,312,478,4009,34
Philip Rivers,33,2009,317,486,4254,28
Philip Rivers,33,2010,357,541,4710,30
Philip Rivers,33,2011,366,582,4624,27
Philip Rivers,33,2012,338,527,3606,26
Philip Rivers,33,2013,378,544,4478,32
Philip Rivers,33,2014,379,570,4286,31
Philip Rivers,33,2010,3025,4678,36655,252
Ben Roethlisberger,32,2004,196,295,2621,17
Ben Roethlisberger,32,2005,168,268,2385,17
Ben Roethlisberger,32,2006,280,469,3513,18
Ben Roethlisberger,32,2007,264,404,3154,32
Ben Roethlisberger,32,2008,281,469,3301,17
Ben Roethlisberger,32,2009,337,506,4328,26
Ben Roethlisberger,32,2010,240,389,3200,17
Ben Roethlisberger,32,2011,324,513,4077,21
Ben Roethlisberger,32,2012,284,449,3265,26
Ben Roethlisberger,32,2013,375,584,4261,28
Ben Roethlisberger,32,2014,408,608,4952,32
Ben Roethlisberger,32,2010,3157,4954,39057,251
Drew Bledsoe,34,1993,214,429,2494,15
Drew Bledsoe,34,1994,400,691,4555,25
Drew Bledsoe,34,1995,323,636,3507,13
Drew Bledsoe,34,1996,373,623,4086,27
Drew Bledsoe,34,1997,314,522,3706,28
Drew Bledsoe,34,1998,263,481,3633,20
Drew Bledsoe,34,1999,305,539,3985,19
Drew Bledsoe,34,2000,312,531,3291,17
Drew Bledsoe,34,2001,40,66,400,2
Drew Bledsoe,34,2002,375,610,4359,24
Drew Bledsoe,34,2003,274,471,2860,11
Drew Bledsoe,34,2004,256,450,2932,20
Drew Bledsoe,34,2005,300,499,3639,23
Drew Bledsoe,34,2006,90,169,1164,7
Drew Bledsoe,34,2010,3839,6717,44611,251
Boomer Esiason,36,1984,51,102,530,3
Boomer Esiason,36,1985,251,431,3443,27
Boomer Esiason,36,1986,273,469,3959,24
Boomer Esiason,36,1987,240,440,3321,16
Boomer Esiason,36,1988,223,388,3572,28
Boomer Esiason,36,1989,258,455,3525,28
Boomer Esiason,36,1990,224,402,3031,24
Boomer Esiason,36,1991,233,413,2883,13
Boomer Esiason,36,1992,144,278,1407,11
Boomer Esiason,36,1993,288,473,3421,16
Boomer Esiason,36,1994,255,440,2782,17
Boomer Esiason,36,1995,221,389,2275,16
Boomer Esiason,36,1996,190,339,2293,11
Boomer Esiason,36,1997,118,186,1478,13
Boomer Esiason,36,2010,2969,5205,37920,247
John Hadle,37,1962,107,260,1632,15
John Hadle,37,1963,28,64,502,6
John Hadle,37,1964,147,274,2157,18
John Hadle,37,1965,174,348,2798,20
John Hadle,37,1966,200,375,2846,23
John Hadle,37,1967,217,427,3365,24
John Hadle,37,1968,208,440,3473,27
John Hadle,37,1969,158,324,2253,10
John Hadle,37,1970,162,327,2388,22
John Hadle,37,1971,233,431,3075,21
John Hadle,37,1972,190,370,2449,15
John Hadle,37,1973,135,258,2008,22
John Hadle,37,1974,142,299,1752,8
John Hadle,37,1975,191,353,2095,6
John Hadle,37,1976,60,113,634,7
John Hadle,37,1977,11,24,76,0
John Hadle,37,2010,2363,4687,33503,244
Y A Tittle,38,1948,161,289,2522,16
Y A Tittle,38,1949,148,289,2209,14
Y A Tittle,38,1950,161,315,1884,8
Y A Tittle,38,1951,63,114,808,8
Y A Tittle,38,1952,106,208,1407,11
Y A Tittle,38,1953,149,259,2121,20
Y A Tittle,38,1954,170,295,2205,9
Y A Tittle,38,1955,147,287,2185,17
Y A Tittle,38,1956,124,218,1641,7
Y A Tittle,38,1957,176,279,2157,13
Y A Tittle,38,1958,120,208,1467,9
Y A Tittle,38,1959,102,199,1331,10
Y A Tittle,38,1960,69,127,694,4
Y A Tittle,38,1961,163,285,2272,17
Y A Tittle,38,1962,200,375,3224,33
Y A Tittle,38,1963,221,367,3145,36
Y A Tittle,38,1964,147,281,1798,10
Y A Tittle,38,2010,2427,4395,33070,242
Tony Romo,34,2004,0,0,0,0
Tony Romo,34,2005,0,0,0,0
Tony Romo,34,2006,220,337,2903,19
Tony Romo,34,2007,335,520,4211,36
Tony Romo,34,2008,276,450,3448,26
Tony Romo,34,2009,347,550,4483,26
Tony Romo,34,2010,148,213,1605,11
Tony Romo,34,2011,346,522,4184,31
Tony Romo,34,2012,425,648,4903,28
Tony Romo,34,2013,342,535,3828,31
Tony Romo,34,2014,304,435,3705,34
Tony Romo,34,2010,2743,4210,33270,242

# 折线图：

import pandas as pd  # 调用pandas库 来读取excell的文件
import scipy.stats as ss  #生成正太分布的库
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv("./data/qb_data.csv")  # 读取excell的文件
sns.set_style(style="whitegrid")  #背景的样式
sns.set_context(context="poster",font_scale=0.8)
sub_df=df.groupby("Age").mean()
# sns.pointplot(sub_df.index,sub_df["Year"])
sns.pointplot(x="Age",y="Cmp",data=df)
plt.show()

#直方图：

import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv("./data/qb_data.csv")  # 读取excell的文件
sns.set_style(style="whitegrid")  #背景的样式
sns.set_context(context="poster",font_scale=0.8)  # context="poster" 文字的格式，font_scale=0.8 字体的大小
f=plt.figure()
f.add_subplot(1,3,1)  #设置在一个页面上的 1行3列第几个
sns.distplot(df["TD"],bins=30)  #kde=False 如果kde 等于false 那么折线图就没有了，如果 hist等于False 那么直方图就没有了
f.add_subplot(1,3,2)
sns.distplot(df["Age"],bins=30)
f.add_subplot(1,3,3)
sns.distplot(df["Cmp"],bins=30)
plt.show()

#箱型图：

import pandas as pd  # 调用pandas库 来读取excell的文件
import scipy.stats as ss  #生成正太分布的库
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv("./data/qb_data.csv")  # 读取excell的文件
sns.set_style(style="whitegrid")  #背景的样式
sns.set_context(context="poster",font_scale=0.8)  # context="poster" 文字的格式，font_scale=0.8 字体的大小
sns.boxplot(x=df["Age"],saturation=0.75,whis=3) # saturation方框的边界   whis上分位数的几倍是他的间距
plt.show()

# 饼图：

import pandas as pd  # 调用pandas库 来读取excell的文件
import scipy.stats as ss  #生成正太分布的库
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv("./data/qb_data.csv")  # 读取excell的文件
# sns.set_style(style="whitegrid")  #背景的样式
# sns.set_context(context="poster",font_scale=0.8)
lbs=df["Age"].value_counts().index   # 这是修饰加上各个形状的分类
explodes=[0.1 if i ==38 else 0 for i in lbs]      #  如果想要着重强调某个部位就是把它分离开...#    这里面我强调的是38的部分
plt.pie(df["Age"].value_counts(normalize=True),explode=explodes,labels=lbs,autopct="%1.1f%%")   #  autopct="%1.1f%%  添加百分比,colors=sns.color_palette("Reds") 指定颜色样式
plt.show()

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