pandas中具有很多很强大的图表绘制功能，今天介绍一个简单好用的绘制数据框DataFrame中某一列数据分布直方图的函数——DataFrame.hist()，顺便介绍一个统计分析函数DataFrame.describe()。
下面通过代码进行展示效果，示例代码中的csv文件是谷歌提供的一个包含加利福尼亚州住房数据的文件。
import pandas as pd
california_housing_dataframe = pd.read_csv("https://storage.googleapis.com/mledu-datasets/california_housing_train.csv", sep=",")
print(california_housing_dataframe)
california_housing_dataframe.describe()以上代码首先通过pandas提供的read_csv()函数读取了csv文件，将其保存为数据框DataFrame类型的数据。然后使用了DataFrame.describe()函数展示了一些有用的统计信息，这个函数在分析一个较大的csv文件时，作为初步的分析工具非常有用。统计结果包括了数据量、均值、方差、最大值、最小值等。以上代码的输出如下：
longitude latitude housing_median_age total_rooms total_bedrooms \
0 -114.31 34.19 15.0 5612.0 1283.0
1 -114.47 34.40 19.0 7650.0 1901.0
2 -114.56 33.69 17.0 720.0 174.0
3 -114.57 33.64 14.0 1501.0 337.0
4 -114.57 33.57 20.0 1454.0 326.0
5 -114.58 33.63 29.0 1387.0 236.0
6 -114.58 33.61 25.0 2907.0 680.0
7 -114.59 34.83 41.0 812.0 168.0
8 -114.59 33.61 34.0 4789.0 1175.0
9 -114.60 34.83 46.0 1497.0 309.0
10 -114.60 33.62 16.0 3741.0 801.0
11 -114.60 33.60 21.0 1988.0 483.0
12 -114.61 34.84 48.0 1291.0 248.0
13 -114.61 34.83 31.0 2478.0 464.0
14 -114.63 32.76 15.0 1448.0 378.0
15 -114.65 34.89 17.0 2556.0 587.0
16 -114.65 33.60 28.0 1678.0 322.0
17 -114.65 32.79 21.0 44.0 33.0
18 -114.66 32.74 17.0 1388.0 386.0
19 -114.67 33.92 17.0 97.0 24.0
20 -114.68 33.49 20.0 1491.0 360.0
21 -114.73 33.43 24.0 796.0 243.0
22 -114.94 34.55 20.0 350.0 95.0
23 -114.98 33.82 15.0 644.0 129.0
24 -115.22 33.54 18.0 1706.0 397.0
25 -115.32 32.82 34.0 591.0 139.0
26 -115.37 32.82 30.0 1602.0 322.0
27 -115.37 32.82 14.0 1276.0 270.0
28 -115.37 32.81 32.0 741.0 191.0
29 -115.37 32.81 23.0 1458.0 294.0
... ... ... ... ... ...
16970 -124.17 40.80 52.0 1606.0 419.0
16971 -124.17 40.80 52.0 1557.0 344.0
16972 -124.17 40.79 43.0 2285.0 479.0
16973 -124.17 40.78 39.0 1606.0 330.0
16974 -124.17 40.77 30.0 1895.0 366.0
16975 -124.17 40.76 26.0 1776.0 361.0
16976 -124.17 40.75 13.0 2171.0 339.0
16977 -124.17 40.62 32.0 1595.0 309.0
16978 -124.18 40.79 39.0 1836.0 352.0
16979 -124.18 40.78 37.0 1453.0 293.0
16980 -124.18 40.78 34.0 1592.0 364.0
16981 -124.18 40.78 33.0 1076.0 222.0
16982 -124.18 40.62 35.0 952.0 178.0
16983 -124.19 41.78 15.0 3140.0 714.0
16984 -124.19 40.78 37.0 1371.0 319.0
16985 -124.19 40.77 30.0 2975.0 634.0
16986 -124.19 40.73 21.0 5694.0 1056.0
16987 -124.21 41.77 17.0 3461.0 722.0
16988 -124.21 41.75 20.0 3810.0 787.0
16989 -124.21 40.75 32.0 1218.0 331.0
16990 -124.22 41.73 28.0 3003.0 699.0
16991 -124.23 41.75 11.0 3159.0 616.0
16992 -124.23 40.81 52.0 1112.0 209.0
16993 -124.23 40.54 52.0 2694.0 453.0
16994 -124.25 40.28 32.0 1430.0 419.0
16995 -124.26 40.58 52.0 2217.0 394.0
16996 -124.27 40.69 36.0 2349.0 528.0
16997 -124.30 41.84 17.0 2677.0 531.0
16998 -124.30 41.80 19.0 2672.0 552.0
16999 -124.35 40.54 52.0 1820.0 300.0
population households median_income median_house_value
0 1015.0 472.0 1.4936 66900.0
1 1129.0 463.0 1.8200 80100.0
2 333.0 117.0 1.6509 85700.0
3 515.0 226.0 3.1917 73400.0
4 624.0 262.0 1.9250 65500.0
5 671.0 239.0 3.3438 74000.0
6 1841.0 633.0 2.6768 82400.0
7 375.0 158.0 1.7083 48500.0
8 3134.0 1056.0 2.1782 58400.0
9 787.0 271.0 2.1908 48100.0
10 2434.0 824.0 2.6797 86500.0
11 1182.0 437.0 1.6250 62000.0
12 580.0 211.0 2.1571 48600.0
13 1346.0 479.0 3.2120 70400.0
14 949.0 300.0 0.8585 45000.0
15 1005.0 401.0 1.6991 69100.0
16 666.0 256.0 2.9653 94900.0
17 64.0 27.0 0.8571 25000.0
18 775.0 320.0 1.2049 44000.0
19 29.0 15.0 1.2656 27500.0
20 1135.0 303.0 1.6395 44400.0
21 227.0 139.0 0.8964 59200.0
22 119.0 58.0 1.6250 50000.0
23 137.0 52.0 3.2097 71300.0
24 3424.0 283.0 1.6250 53500.0
25 327.0 89.0 3.6528 100000.0
26 1130.0 335.0 3.5735 71100.0
27 867.0 261.0 1.9375 80900.0
28 623.0 169.0 1.7604 68600.0
29 866.0 275.0 2.3594 74300.0
... ... ... ... ...
16970 891.0 367.0 1.5850 75500.0
16971 758.0 319.0 1.8529 62500.0
16972 1169.0 482.0 1.9688 70500.0
16973 731.0 327.0 1.6369 68300.0
16974 990.0 359.0 2.2227 81300.0
16975 992.0 380.0 2.8056 82800.0
16976 951.0 353.0 4.8516 116100.0
16977 706.0 277.0 2.8958 86400.0
16978 883.0 337.0 1.7450 70500.0
16979 867.0 310.0 2.5536 70200.0
16980 950.0 317.0 2.1607 67000.0
16981 656.0 236.0 2.5096 72200.0
16982 480.0 179.0 3.0536 107000.0
16983 1645.0 640.0 1.6654 74600.0
16984 640.0 260.0 1.8242 70000.0
16985 1367.0 583.0 2.4420 69000.0
16986 2907.0 972.0 3.5363 90100.0
16987 1947.0 647.0 2.5795 68400.0
16988 1993.0 721.0 2.0074 66900.0
16989 620.0 268.0 1.6528 58100.0
16990 1530.0 653.0 1.7038 78300.0
16991 1343.0 479.0 2.4805 73200.0
16992 544.0 172.0 3.3462 50800.0
16993 1152.0 435.0 3.0806 106700.0
16994 434.0 187.0 1.9417 76100.0
16995 907.0 369.0 2.3571 111400.0
16996 1194.0 465.0 2.5179 79000.0
16997 1244.0 456.0 3.0313 103600.0
16998 1298.0 478.0 1.9797 85800.0
16999 806.0 270.0 3.0147 94600.0
[17000 rows x 9 columns]longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value
count 17000.000000 17000.000000 17000.000000 17000.000000 17000.000000 17000.000000 17000.000000 17000.000000 17000.000000
mean -119.562108 35.625225 28.589353 2643.664412 539.410824 1429.573941 501.221941 3.883578 207300.912353
std 2.005166 2.137340 12.586937 2179.947071 421.499452 1147.852959 384.520841 1.908157 115983.764387
min -124.350000 32.540000 1.000000 2.000000 1.000000 3.000000 1.000000 0.499900 14999.000000
25% -121.790000 33.930000 18.000000 1462.000000 297.000000 790.000000 282.000000 2.566375 119400.000000
50% -118.490000 34.250000 29.000000 2127.000000 434.000000 1167.000000 409.000000 3.544600 180400.000000
75% -118.000000 37.720000 37.000000 3151.250000 648.250000 1721.000000 605.250000 4.767000 265000.000000
max -114.310000 41.950000 52.000000 37937.000000 6445.000000 35682.000000 6082.000000 15.000100 500001.000000下面就可以介绍今天的重点内容了：DataFrame.hist()。通过下面一行代码，就能简单迅速的画出某一列中的值的分布直方图：
california_housing_dataframe.hist('housing_median_age')