import pandas as pd df = pd.DataFrame([['zs', 12], ['ls', 4]], columns = ['Name','Age']) df2 = pd.DataFrame([['ww', 16], ['zl', 8]], columns = ['Name','Age']) df = df.append(df2) # 删除index为0的行 df = df.drop(0)
# 这个循环,每次取出一列数据,然后用均值来填充 for i in movie.columns: if np.all(pd.notnull(movie[i])) == False: print(i) movie[i].fillna(movie[i].mean(), inplace=True) 6.2.3 不是缺失值nan,有默认标记的 直接看例子: 数据是这样的: # 读入数据 wis = pd.read_csv("https://arc...
append(len(l1)) #连续天数 df = pd.DataFrame({'时间': result1, '连续掉线天数': result2}) return df.reindex(columns=["建筑编号", "时间", "连续掉线天数"], fill_value="{0}".format(BUILD_ID)) def main_process(self,df): df1=pd.DataFrame(df[["BUILD_ID","BUILD_NAME","OFF_TIME"...
# for i in X_df: # X_ret = pd.concat([X_ret, X_df[i] * y_.values], axis=1) # print(i) # # # 方法1超级慢 # y_ = y_.astype(np.float16) # X_ret = pd.DataFrame(index=X_df.index, columns=X_df.columns) # for i in X_df: # X_ret[i] = X_df[i] * y_.va...
这一期,主要是利用python处理非结构化文本数据。而且每个小案例可能隐藏着一些使用的Pandas技巧. 嵌套json展开 隐藏知识点:函数递归 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # ⚠️注意:用`json.loads`处理json型字符串时,键值应用双引号,外围用单引号。否则会转换失败,这里只是简单处理,所以采用`eval...
unless it is passed, in which case the values will beselected (see below). Any None objects will be dropped silently unlessthey are all None in which case a ValueError will be raised.axis : {0/'index', 1/'columns'}, default 0The axis to concatenate along.join : {'inner', 'outer'...
(np.where(dates == 0))[0] last_friday = np.ravel(np.where(dates == 4))[-1] #创建一个数组,用于存储三周内每一天的索引值 weeks_indices = np.arange(first_monday, last_friday + 1) #按照每个子数组5个元素,用split函数切分数组 weeks_indices = np.split(weeks_indices, 5) #output [...
select wm_concat(column_name) from user_tab_columns where table_name=upper('tableName') 获取某个表的建表语句(可以看字段类型) select dbms_metadata.get_ddl('TABLE',upper('表名')) from dual; #查询表字段类型 select * from all_tab_cols where table_name=’大写表名’ ...
probList.append((probArray[i])[1]) probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ...
import sys sys.path.append('/home/aistudio/external-libraries') 使用git命令来同步代码 (暂时需要Paddle 1.4.1以上) 例如:In [6] %cd work/ /home/aistudio/work In [7] !git clone https://github.com/PaddlePaddle/Paddle.git #Paddle官方模型 正克隆到 'Paddle'... remote: Enumerating objects...