[Übersicht über künstliche Intelligenz] Ausreißer herausfiltern und durch nan ersetzen, alle Zeitstempel generieren (der Wert ist nan), nan durch den Durchschnitt der ersten fünf ersetzen, die Dateien zusammenführen und drei Dezimalstellen beibehalten.

[Übersicht über künstliche Intelligenz] Ausreißer herausfiltern und durch nan ersetzen, alle Zeitstempel generieren (der Wert ist nan), nan durch den Durchschnitt der ersten fünf ersetzen, die Dateien zusammenführen und drei Dezimalstellen beibehalten.

---

1. Ausreißer filtern und durch Nan ersetzen

import os
import numpy as np
import pandas as pd

dataset_folder = './datasets_init/'
file_list = os.listdir(dataset_folder)  # 确定数据集路径

for filename in file_list:  # 遍历数据集路径下的全部文件
    if filename.endswith('.csv'):  # 对csv文件进行操作
        file_name = os.path.join(dataset_folder, filename)
        file_save_path = os.path.join('./datasets/',filename)
        df = pd.read_csv(file_name, header=0, index_col=0)
        df.fillna('NAN',inplace=True)
        
        col_num  = df.shape[1]
        
        # 求均值、方差用z
        for cow_index in range(col_num):
            cow_data = df.iloc[:,cow_index].values # 获取每列的数据
            cow_data_len = len(cow_data) # 获取每列的长度
            data_list = []
            for row in range(cow_data_len):
                data = cow_data[row] 
                if data == 'NAN':
                    pass
                else:
                    data_list.append(data)
            # print(data_list)
            data_list = np.array(data_list)
            data_list = data_list.astype(np.float32)
            mean = data_list.mean()
            std = data_list.std()
            for row in range(cow_data_len):
                data = cow_data[row] 
                if data == 'NAN':
                    pass
                else:
                    data = float(data)
                    z_s = np.abs((data - mean)/std)
                    if z_s > 3:
                        df.iloc[row,cow_index] = 'NAN'
                    if row > 5:
                        data_1 = float(cow_data[row-1])
                        data_2 = float(cow_data[row-2])
                        data_3 = float(cow_data[row-3])
                        data_4 = float(cow_data[row-4])
                        data_5 = float(cow_data[row-5])
                        data_eva = ( data_1 + data_2 + data_3 + data_4 + data_5 ) / 5
                        p = np.abs((data - data_eva)/(data_eva+0.0001)) # 加0.0001是为了避免除以零的事件发生
                        if p > 5:
                            df.iloc[row,cow_index] = 'NAN'
        df.to_csv(file_save_path)

2. Alle Zeitstempel generieren (Wert ist nan)

import numpy as np
import pandas as pd
example = '2017-01-27 17:01:55'
years = ['2017']
month = ['01','02','03','04','05','06','07','08','09','10','11','12']
b_m = ['01','03','05','07','08','10','12']
s_m = ['04','06','09','11']
day1 = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30','31']
day2 = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30']
day3 = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28']
hour = ['00','01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23']
TTIMESTAMP = [] # 保留时间戳
for year in years:
    for i in month:
        if i in b_m:
            for j in day1:
                for k in hour:
                    time = year+'-'+i+'-'+j+' '+k+':00:00'
                    TTIMESTAMP.append(time)
                    time = year+'-'+i+'-'+j+' '+k+':30:00'
                    TTIMESTAMP.append(time)
        elif i in s_m:
            for j in day2:
                for k in hour:
                    time = year+'-'+i+'-'+j+' '+k+':00:00'
                    TTIMESTAMP.append(time)
                    time = year+'-'+i+'-'+j+' '+k+':30:00'
                    TTIMESTAMP.append(time)
        else:
            for j in day3:
                for k in hour:
                    time = year+'-'+i+'-'+j+' '+k+':00:00'
                    TTIMESTAMP.append(time)
                    time = year+'-'+i+'-'+j+' '+k+':30:00'
                    TTIMESTAMP.append(time)


data = ['NAN' for i in range(17520)] # 生成每列的初始值
df = pd.DataFrame(data={
    
    
    'TTIMESTAMP':TTIMESTAMP,
    'Temp_C':data,
    'SpCond_mS':data,
    'Cond_mS':data,
    'Sal':data,
    'DO_percent':data,
    'DO_ppm':data,
    'pH':data,
    'pH_mV':data,
    'Turb_NTU':data,
    'Chl_ppb':data,
    'Chl_RFU':data,
    'PE_uL':data,
    'PE_RFU':data
})
df.to_csv('05-17.csv',index=False)

3. Ersetzen Sie nan durch den Durchschnitt der ersten fünf

import os
import numpy as np
import pandas as pd

dataset_folder = './datasets/'
file_list = os.listdir(dataset_folder)  # 确定数据集路径

for filename in file_list:  # 遍历数据集路径下的全部文件
    if filename.endswith('.csv'):  # 对csv文件进行操作
        file_name = os.path.join(dataset_folder, filename)
        df = pd.read_csv(file_name, header=0, index_col=0)
        # df.fillna('NAN',inplace=True)
        
        for cow_index in range(13):
            cow_data = df.iloc[:,cow_index].values # 获取每列的数据
            cow_data_len = len(cow_data) # 获取每列的长度
            data_list = []
            for row in range(cow_data_len):
                data = cow_data[row] 
                if data == 'NAN':
                    data_1 = float(cow_data[row-1])
                    data_2 = float(cow_data[row-2])
                    data_3 = float(cow_data[row-3])
                    data_4 = float(cow_data[row-4])
                    data_5 = float(cow_data[row-5])
                    data_eva = ( data_1 + data_2 + data_3 + data_4 + data_5 ) / 5
                    df.iloc[row,cow_index] = data_eva
                else:
                    pass
        df.to_csv(file_name)

4. Dateien zusammenführen und drei Dezimalstellen beibehalten

# 把文件进行拼接，并且强制所有数据保留三位小数，最终保存成类似 '../SeaDataset/05/data_05-17-19.csv' 的形式
# 注意： 中间会产生类似 '../SeaDataset_Init/05/05-17-19.csv' 的中间文件， 试验完自动删掉
# 还可以继续改进，比如：引入for循环进行批量处理

import os
import numpy as np
import pandas as pd


# 合并文件
df1 = pd.read_csv('../SeaDataset_Init/03/03-17.csv', header= 0, index_col= 0)
df2 = pd.read_csv('../SeaDataset_Init/03/03-18-19.csv', header= 0, index_col= 0)

df = pd.concat([df1, df2], axis=0, join='outer', ignore_index= False)

df.to_csv('../SeaDataset_Init/03/03-17-19.csv')


# 保留三位小数
df = pd.read_csv('../SeaDataset_Init/03/03-17-19.csv', header= 0, index_col= 0)
# print(type(df.iloc[0,0]))
df_col = df.columns.tolist()
# print(df_col)
df_data = df.iloc[:,:].values
df_data = np.around(df_data, 3)
# print(type(df_data))
df_data = pd.DataFrame(data=df_data, columns=df_col)
# print(type(df_data.iloc[0,0]))
# print(df_data)

df = pd.read_csv('../SeaDataset_Init/03/03-17-19.csv', header= 0)
df_index = df.iloc[:, 0]
# print(df_index)

df = pd.concat([df_index, df_data], axis= 1, join= 'outer', ignore_index=False)
# print(df)
# print(df.iloc[0,1])
# print(type(df.iloc[0,1]))
df.to_csv('../SeaDataset/03/data_03-17-19.csv', index=False)   # 不要pd给分配的index


# 最终验证是否符合原本的形状
df = pd.read_csv('../SeaDataset/03/data_03-17-19.csv', header= 0, index_col= 0)
print(df.iloc[0,0])   # 一个小数，表示的是温度
print(type(df.iloc[0,0])) #  应该是numpy.float
print(df) # 检查格式，数据数量

# 删除中间文件
os.remove('../SeaDataset_Init/03/03-17-19.csv')