# 1.创建新的DataFrame dataset_bin = pd.DataFrame() # 包含所有离散后的值 dataset_con = pd.DataFrame() # 包含所有未离散的值 # 2.predclass标签属性, 预测目标:转换为0/1,年收入超过50k记为1. # 转换 dataset_raw.loc[dataset_raw['predclass']=='>50K', 'predclass'] = 1 dataset_raw.loc[...
DataFrame(data) statistics = df['身高(cm)'].describe() print(statistics) count 12.000000 mean 125.858333 std 9.947997 min 104.600000 25% 122.450000 50% 126.000000 75% 131.725000 max 141.500000 Name: 身高(cm), dtype: float64 统计描述提供了以下信息: count:非缺失值的个数。 mean:平均值。 std:...
df1 ,df2,df3,df4 ,df5= statistics(result.T),statistics(difference.T),statistics(diff_week),min_result.reset_index(),max_result.reset_index() df6,df7= pd.DataFrame(result.quantile(0.05,axis=1)),pd.DataFrame(max_d) adds = ['_原始','_日差分','_周差分','_min','_max'] new = ...
Summary Statistics by Level# 可以在某一个 level 上进行数据统计,书中拿 sum 来举例。主要涉及的参数是 level 和 axis。 Indexing with a DataFrame's columns# set_index可以把 columns 当做 DataFrame 的 index,返回一个新的 DataFrame: reset_index可以把分层 index 都还原到 columns 上。 Combining and Mer...
Chapter 5 - Basic Math and Statistics Segment 3 - Generating summary statistics using pandas and scipy importnumpyasnpimportpandasaspdfrompandasimportSeries, DataFrameimportscipyfromscipyimportstats address ='~/Data/mtcars.csv'cars = pd.read_csv(address) ...
DataFrame(bic_matrix) # 从中可以找出最小值 p,q = bic_matrix.stack().idxmin() # 先用stack展平,然后用idxmin找出最小值位置。 print(u'BIC最小的p值和q值为:%s、%s' %(p,q)) model = ARIMA(data, (p,1,q)).fit() # 建立ARIMA(0, 1, 1)模型 model.summary2() # 给出一份模型报告 ...
X_train,X_test,y_train,y_test=generate_data(n_train=n_train,n_test=n_test,n_features=n_features,contamination=contamination,random_state=123)X_train_pd=pd.DataFrame(X_train)X_train_pd.head() image image 将树的大小max_samples设置为 40 个观测值。在 IForest 中,较小的样本量可以生成更好...
defdescriptive_stat_threshold(df,pred_score,threshold):# Let's see how many '0's and '1's.df=pd.DataFrame(df)df['Anomaly_Score']=pred_score df['Group']=np.where(df['Anomaly_Score']<threshold,'Normal','Outlier')# Nowlet's show the summary statistics:cnt=df.groupby('Group')['Ano...
Lastly, you call buildings.describe() to get summary statistics for each column in the DataFrame. This is one of the best ways to get a quick feel for the nature of the dataset that you’re working with. Here’s what each row returned from .describe() means: count is the number of ...
·Describe DataFrame columns>>> df.columnsIndex(['Country','Capital','Population'], dtype='object')l ·Info on DataFrame>>> df.infoRangeIndex: 3 entries, 0 to 2Datacolumns(total3columns):Country 3 non-null objectCapital 3 non-null objectPopulation 3 non-null objectdtypes:object(3)memory...