通过DataFrame()函数包裹二维数组可以创建一个DataFrame对象,可以通过参数index、columns指定行标签和列标签。也可以通过python的字典类型初始化DataFrame,其键名默认为列标签 import pandas as pd import numpy as np # 通过一维数组初始化Series s = pd.Series([1, 2.0, np.nan, 'test']) print(s) # 通过二维...
第三个参数 columns= 为列的名字(为list形式) dates = pd.date_range('20200627',periods=6) df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d']) print(df) 1. 2. 3. np.random.randn(6,4)函数表示生成正态分布的随机数矩阵。 index表示给每一行命名,columns表...
8, 9] }) # 删除列 B data_new = data.drop(columns=["B"]) print(data_new) # 或使用...
intf_df = pd.DataFrame(raw_data) print(intf_df) ''' Dataframe从打印的结果可以看到 是一种二维矩阵的数据,非常符合我们的使用习惯 name desc 0 Eth1/1 netdevops1 1 Eth1/2 netdevops2 ''' intf_df.to_csv('as01_info.csv', index=False, columns=['name', 'desc'])...
{USER_NAME};PWD={PASSWORD}') cursor = cnxn.cursor() cursor.execute("EXECUTE [dbo].[PyPlotMatplotlib]") tables = cursor.fetchall() for i in range(0, len(tables)): fig = pickle.loads(tables[i][0]) fig.savefig(str(i)+'.png') print("The plots are saved in directory: ",os....
merge(data1,data2,on=[a],how='left') pd.concat([data1,data2]) # 合并,与merge的区别,自查**(特别注意要使用[])** pd.pivot_table( data ) # 用df做data透视表(类似于Excel的数透) data.reset_index() # 修改、删除原有索引 data.reindex() # 重置索引,如下示例 data=data.reindex(columns...
y_test_scores=knn.decision_function(X_test)y_test_pred=knn.predict(X_test)# outlierlabels(0or1)defcount_stat(vector):# Because it is'0'and'1',we can run a count statistic.unique,counts=np.unique(vector,return_counts=True)returndict(zip(unique,counts))print("The training data:",count...
GROUP BY sr_customer_sk ) returned ON ss_customer_sk=sr_customer_sk'''# Define the columns we wish to import.column_info = {"customer": {"type":"integer"},"orderRatio": {"type":"integer"},"itemsRatio": {"type":"integer"},"frequency": {"type":"integer"} ...
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'...
(total 5 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Age 199 non-null int64 1 Sex 199 non-null object 2 Blood Pressure Levels (BP) 199 non-null object 3 Na to Potassium Ration(Na_to_K) 199 non-null float64 4 Drug Class 199 non-null object dtypes: float64(...