程序代写代做代考 housePrice-checkpoint

housePrice-checkpoint

In [1]:

import numpy as np
import pandas as pd

In [2]:

df = pd.read_csv(‘train.csv’)

df.head()

Out[2]:

Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities … PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub … 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub … 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub … 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub … 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub … 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 81 columns

In [5]:

import matplotlib.pyplot as plt
%matplotlib inline

In [8]:

#sns.distplot(df[‘SalePrice’], kde = False, color = ‘b’, hist_kws={‘alpha’: 0.9})’

plt.hist(df[‘SalePrice’], 100, normed=1, facecolor=’green’, alpha=0.5)

Out[8]:

(array([ 4.75580827e-07, 0.00000000e+00, 4.75580827e-07,
5.70696992e-07, 5.70696992e-07, 6.65813158e-07,
3.04371729e-06, 2.75836880e-06, 2.18767180e-06,
3.32906579e-06, 5.61185376e-06, 6.37278308e-06,
6.94348007e-06, 8.75068721e-06, 8.46533872e-06,
8.65557105e-06, 5.89720225e-06, 5.70696992e-06,
5.42162143e-06, 6.94348007e-06, 6.37278308e-06,
5.13627293e-06, 4.28022744e-06, 3.13883346e-06,
2.85348496e-06, 2.94860113e-06, 2.94860113e-06,
3.13883346e-06, 2.47302030e-06, 2.18767180e-06,
1.61697481e-06, 1.80720714e-06, 1.71209098e-06,
1.71209098e-06, 1.33162632e-06, 1.04627782e-06,
4.75580827e-07, 7.60929323e-07, 1.23651015e-06,
1.14139398e-06, 9.51161654e-07, 5.70696992e-07,
5.70696992e-07, 4.75580827e-07, 9.51161654e-08,
2.85348496e-07, 4.75580827e-07, 5.70696992e-07,
4.75580827e-07, 3.80464661e-07, 2.85348496e-07,
1.90232331e-07, 2.85348496e-07, 9.51161654e-08,
2.85348496e-07, 9.51161654e-08, 1.90232331e-07,
1.90232331e-07, 0.00000000e+00, 1.90232331e-07,
0.00000000e+00, 9.51161654e-08, 9.51161654e-08,
0.00000000e+00, 9.51161654e-08, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
9.51161654e-08, 0.00000000e+00, 0.00000000e+00,
1.90232331e-07, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 9.51161654e-08, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 9.51161654e-08,
9.51161654e-08, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 9.51161654e-08,
9.51161654e-08]),
array([ 34900., 42101., 49302., 56503., 63704., 70905.,
78106., 85307., 92508., 99709., 106910., 114111.,
121312., 128513., 135714., 142915., 150116., 157317.,
164518., 171719., 178920., 186121., 193322., 200523.,
207724., 214925., 222126., 229327., 236528., 243729.,
250930., 258131., 265332., 272533., 279734., 286935.,
294136., 301337., 308538., 315739., 322940., 330141.,
337342., 344543., 351744., 358945., 366146., 373347.,
380548., 387749., 394950., 402151., 409352., 416553.,
423754., 430955., 438156., 445357., 452558., 459759.,
466960., 474161., 481362., 488563., 495764., 502965.,
510166., 517367., 524568., 531769., 538970., 546171.,
553372., 560573., 567774., 574975., 582176., 589377.,
596578., 603779., 610980., 618181., 625382., 632583.,
639784., 646985., 654186., 661387., 668588., 675789.,
682990., 690191., 697392., 704593., 711794., 718995.,
726196., 733397., 740598., 747799., 755000.]),
)

([,
,
],
)