data["department_name_NUMERIC"] = data["department_name"].cat.codes data["job_title_NUMERIC_NUMERIC"...] = data["job_title"].cat.codes data["gender_short_NUMERIC"] = data["gender_short"].cat.codes data["termreason_desc_NUMERIC..."] = data["termreason_desc"].cat.codes data["termtype_desc_NUMERIC"] = data["termtype_desc"].cat.codes...] = data["job_title"].cat.codes data["gender_short_NUMERIC"] = data["gender_short"].cat.codes data["termreason_desc_NUMERIC..."] = data["termreason_desc"].cat.codes data["termtype_desc_NUMERIC"] = data["termtype_desc"].cat.codes
df['gender'] = df['gender'].astype('category') df['gender_cat'] = df['gender'].cat.codes df['SeniorCitizen...'] = df['SeniorCitizen'].astype('category') df['SeniorCitizen_cat'] = df['SeniorCitizen'].cat.codes...InternetService'] = df['InternetService'].astype('category') df['InternetService_cat'] = df['InternetService'].cat.codes...'] = df['DeviceProtection'].astype('category') df['DeviceProtection_cat'] = df['DeviceProtection'].cat.codes...churn列,也就是我们的目标列做一类似的操作: df['Churn'] = df['Churn'].astype('category') df['Churn_cat'] = df['Churn'].cat.codes
要执行此技术,我们可以使用Pandas: categorical_data["species_cat"] = categorical_data["species"].cat.codes categorical_data...["island_cat"] = categorical_data["island"].cat.codes categorical_data["sex_cat"] = categorical_data[..."sex"].cat.codes categorical_data.head() 如您所见,添加了三个新功能,每个功能都包含编码的分类功能。...好的看看如何在代码中做到这一点: categorical_data["species"] = categorical_data["species"].cat.codes island_means =...feature_sel_data["sex"] = feature_sel_data["sex"].cat.codes # Use 3 features selector = SelectKBest
astype("category") # 建立稀疏矩阵 triplesplays = coo_matrix((data['plays'].astype(float), (data['artist'].cat.codes..., data['user'].cat.codes))) 这里返回的矩阵有300,000名艺术家和360,000名用户,总共有大约1700万条目。
df["sales"] = df["sales"].astype('category') df["salary"] = df["salary"].astype('category') 然后再使用 cat.codes...来实现对整数的映射 df["sales"] = df["sales"].cat.codes df["salary"] = df["salary"].cat.codes ?
categorical variables df['Category'] = df['Category'].astype('category') df['Category'] = df['Category'].cat.codes
navy', 'periwinkle', 'rose'], dtype='object') 实际上,对于开始的整数类型映射,我们可以先通过 reorder_categories 进行重新排序,然后再使用 cat.codes...>>> ccolors.cat.reorder_categories(mapper).cat.codes 0 0 1 1 2 2 3 0 4 2 5 3 6 3
'navy', 'periwinkle', 'rose'], dtype='object') 实际上,对于开始的整数类型映射,我们可以先通过reorder_categories进行重新排序,然后再使用cat.codes...>>> ccolors.cat.reorder_categories(mapper).cat.codes 0 0 1 1 2 2 3 0 4 2 5 3 6 3
7, 7)) ax.scatter( X_reduced[:, 0], X_reduced[:, 1], c=node_subjects.astype("category").cat.codes
y=subsetData['gdpPercap'], s=subsetData['pop']/200000 , c=subsetData['continent'].cat.codes
categorical_covariates_num_embeddings = [] for col in categorical_covariates: data_all[col] = data_all[col].astype('category').cat.codes...categorical_static_num_embeddings = [] for col in categorical_static: data_all[col] = data_all[col].astype('category').cat.codes
transform the old column name in something numeric df[ X ]=pd.Categorical(df[ X ]) df[ X ]=df[ X ].cat.codes
80000080 %time _ = labels.astype('category') Wall time: 444 ms 分类方法 先使用分类入口:cat方法;再使用codes,categories方法 cat.codes
hours-per-week', 'native-country', 'income'] # 标签转换 data['income'] = data['income'].astype("category").cat.codes
for col in cat_cols: X[col]=X[col].astype(“category”) X[col]=X[col].cat.codes X.head() ?
DESTINATION_AIRPORT","ORIGIN_AIRPORT"] for item in cols: data[item] = data[item].astype("category").cat.codes
DESTINATION_AIRPORT", "ORIGIN_AIRPORT"] for item in cols: data[item] = data[item].astype("category").cat.codes...DESTINATION_AIRPORT", "ORIGIN_AIRPORT"] for item in cols: data[item] = data[item].astype("category").cat.codes
Scatterplot on main ax ax_main.scatter('displ', 'hwy', s=df.cty*4, c=df.manufacturer.astype('category').cat.codes...Scatterplot on main ax ax_main.scatter('displ', 'hwy', s=df.cty*5, c=df.manufacturer.astype('category').cat.codes
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