用pandas读取csv文件，并删除包含NaN的数值行，计算df列的length

Sn78-2-d - 副本.CSV

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read_cellz_test.py

# -*- coding: UTF-8 -*-

# 自动读取手机应用“Cellular_Z的csv日志格式”计算rsrp平均值的脚本
import os
from statistics import mean
import pandas as pd
import re

# 给定待读取的csv文件的路径到列表里
csv_filepath = r"D:\\kang\\myproject\\read_RSRP_four_direction_csv_avg\\report\\Sn78-2-d - 副本.CSV"


# df1 = df.iloc[:, 0:4]  #读取第1列到第4列数据


###1.跳过每个csv文件开头不需要的行的函数
# def skip_to(fle,**kwargs):
#     if os.stat(fle).st_size == 0:
#         raise ValueError("File is empty")
#     with open(fle) as f:
#         pos = 0
#         cur_line = f.readline()
#         while not cur_line.find('下行MAC总吞吐率(bps)","上行MAC总吞吐率(bps)')>=0:
#             pos = f.tell()
#             cur_line = f.readline()
#         f.seek(pos)
#         return pd.read_csv(f, **kwargs)

# 2.读取csv文件到内存
# df = skip_to(csv_filepath,encoding = 'gbk')  # 读取csv用模块skip_to()

# 1、加载指定的csv文件数据列到内存，优化程序速度
df = pd.read_csv(csv_filepath,encoding = 'utf-8',usecols=[0,11,20])  #读取csv文件到内存,读取指定的[0,11,20] 列
# 2、根据实际的5G？4G？情况处理加载初始数据列
b_SS_RSRP = df['SS-RSRP'].isnull().all()  # 判断SS-RSRP列是否全部为NaN
b_RSRP = df['RSRP'].isnull().all()  # 判断RSRP列是否全部为NaN

if b_RSRP==True and b_SS_RSRP==False:  #测试5G
    df1 = df[["TIME","SS-RSRP"]] 
elif b_RSRP==False and b_SS_RSRP==True:  #测试4G
    df1 = df[["TIME","RSRP"]] 
else:  #异常情况，需要打印提示
    print("-----抓取cell_z缓存csv日志步骤出错，请先退出程序重试！-----")

# 3、处理含有NaN的数据行
df2 = df1.dropna(how="any")
temp = df2.index
dflen = len(temp)  #求出df对象的长度
# df1.loc[[i],["SS-RSRP"]]

# 4、根据正则表达式提取
pattern_1 = r"^\d{8}.*\d{2}:\d{2}:\d{2}"
avg_index_list = []
rsrp_list = []
for i in range(dflen):
    result_l = re.findall(pattern= pattern_1 , string= df2.iloc[i,0] )  # 索引值是从0开始，并且是去除了NaN的那个df对象，从新开始数的
    avg_index_list.append( result_l[0] )
    rsrp_list.append( df2.iloc[i,1] )
# print(avg_index_list)  #获得1个由时间构成的列表，相当于Excel的用，文本分列    
# print(rsrp_list)  #获得1个由信号强度构成的列表，相当于Excel的用，文本分列 
# 5、使用zip()内置函数创建DataFrame
zip_result = zip(avg_index_list,rsrp_list)
zip_result_list = list(zip_result)
df3 = pd.DataFrame(zip_result_list,columns=["时间","数值"])
#  6、数据分组计算平均值
_df_count = df3.groupby("时间").count()   #计数
_df_mean = df3.groupby("时间").mean()  #平均值
_df_mean_all = _df_mean["数值"].mean() #数据透视表求Cellular_Z.apk输出日志的RSRP真实平均值
#  7、对浮点值进行小数点后7位处理化简
_df_mean_all_round = round( _df_mean_all ,7)



# print(df2)
# print(result_l)
# print(temp)
# print(dflen)
# print(b_SS_RSRP)  #Flase
# print(b_RSRP)  #True
# print(zip_result_list)
# print(df3)
# print(_df_mean_all)  #-110.06315789473685

print(_df_mean_all_round)