pandas常用数据分析语法（二）：概括分析和图

pandas常用数据分析语法（二）：概括分析和图

数据描述

data_train['SalePrice'].describe()

count      1460.000000
mean     180921.195890
std       79442.502883
min       34900.000000
25%      129975.000000
50%      163000.000000
75%      214000.000000
max      755000.000000
Name: SalePrice, dtype: float64

直方图

#图
sns.distplot(data_train['SalePrice'])
#数据
print("Skewness: %f" % data_train['SalePrice'].skew())
print("Kurtosis: %f" % data_train['SalePrice'].kurt())

箱子图，散点图

# 箱子图
var = 'CentralAir'
data = pd.concat([data_train['SalePrice'], data_train[var]], axis=1)
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);

# 散点图
var = 'YearBuilt'
data = pd.concat([data_train['SalePrice'], data_train[var]], axis=1)
data.plot.scatter(x=var, y="SalePrice", ylim=(0, 800000))

关系矩阵

#无数字
corrmat = data_train.corr()
f, ax = plt.subplots(figsize=(20, 9))
sns.heatmap(corrmat, vmax=0.8, square=True)

#有数字
k  = 10 # 关系矩阵中将显示10个特征
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(data_train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, \
                 square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()

参考：
https://www.kaggle.com/marsggbo/kaggle