嗨，我仍然是蟒蛇新手和学习，因为我要去。我正在尝试读取包含3列的CSV文件，前2列是坐标，第3列是值。 bellow是CSV文件内容的示例。
322000.235 582999.865 149.309
322000.235 582999.615 149.29
322000.235 582999.365 149.276
322000.235 582999.115 149.26
322000.235 582998.865 149.246
322000.235 582998.615 149.245
322000.235 582998.365 149.235
322000.235 582998.115 149.228
322000.235 582997.865 149.223
322000.235 582997.615 149.226
322000.485 582999.865 149.249
322000.485 582999.615 149.217
322000.485 582999.365 149.224
322000.485 582999.115 149.243
322000.485 582998.865 149.249
322000.485 582998.615 149.256
322000.485 582998.365 161.259
322000.485 582998.115 149.257
322000.485 582997.865 149.26
322000.485 582997.615 149.274
322000.735 582999.865 149.193
322000.735 582999.615 149.159
322000.735 582999.365 149.179
322000.735 582999.115 149.215
322000.735 582998.865 149.242
322000.735 582998.615 149.261
322000.735 582998.365 160.274
322000.735 582998.115 149.29
322000.735 582997.865 149.321
322000.735 582997.615 149.342
322000.985 582999.865 149.156
322000.985 582999.615 149.128
322000.985 582999.365 149.16
322000.985 582999.115 149.205
322000.985 582998.865 149.239
322000.985 582998.615 149.265
322000.985 582998.365 149.289
322000.985 582998.115 149.324
322000.985 582997.865 149.373
322000.985 582997.615 149.401
我需要它以下面的内容阅读
(322000.235 582999.865 149.309 ) (322000.485 582999.865 149.249 ) (322000.735 582999.865 149.193 ) (322000.985 582999.865 149.156 )
(322000.235 582999.615 149.29 ) (322000.485 582999.615 149.217 ) (322000.735 582999.615 149.159 ) (322000.985 582999.615 149.128 )
(322000.235 582999.365 149.276 ) (322000.485 582999.365 149.224 ) (322000.735 582999.365 149.179 ) (322000.985 582999.365 149.16 )
(322000.235 582999.115 149.26 ) (322000.485 582999.115 149.243 ) (322000.735 582999.115 149.215 ) (322000.985 582999.115 149.205 )
(322000.235 582998.865 149.246 ) (322000.485 582998.865 149.249 ) (322000.735 582998.865 149.242 ) (322000.985 582998.865 149.239 )
(322000.235 582998.615 149.245 ) (322000.485 582998.615 149.256 ) (322000.735 582998.615 149.261 ) (322000.985 582998.615 149.265 )
(322000.235 582998.365 149.235 ) (322000.485 582998.365 161.259 ) (322000.735 582998.365 160.274 ) (322000.985 582998.365 149.289 )
(322000.235 582998.115 149.228 ) (322000.485 582998.115 149.257 ) (322000.735 582998.115 149.29 ) (322000.985 582998.115 149.324 )
(322000.235 582997.865 149.223 ) (322000.485 582997.865 149.26 ) (322000.735 582997.865 149.321 ) (322000.985 582997.865 149.373 )
(322000.235 582997.615 149.226 ) (322000.485 582997.615 149.274 ) (322000.735 582997.615 149.342 ) (322000.985 582997.615 149.401 )
我把第3列的内容放在一起，然后使用.shift（-1）和.shift（1）将值检查到其相邻值，这可以完成工作，但是我得到了不必要的数据，实际上是我的想要的不仅仅是检查它旁边的值，还要将其检查为具有相邻值的网格，在大多数情况下这将是4次检查。示例链接，红色是用于检查所有相邻蓝色标记的值。 example_value check 这是我到目前为止的脚本，希望所有这些都是足够的信息和可理解的不知道我是否可以改变它或应该重新开始以及如何。希望有人可以提供帮助。
from __future__ import print_function
import pandas as pd
import os
import re
Dir = os.getcwd()
Blks = []
CSV = []
for files in Dir:
for f in os.listdir(Dir):
if re.search('.txt', f):
Blks = [each for each in os.listdir(Dir) if each.endswith('.txt')]
print (Blks)
for files in Dir:
for f in os.listdir(Dir):
if re.search('.csv', f):
CSV = [each for each in os.listdir(Dir) if each.endswith('.csv')]
print (CSV)
limit = 3
tries = 0
while True:
print ("----------------------------------------------------")
spikewell = float(raw_input("Please Enter Parameters: "))
tries += 1
if tries == 4:
print ("----------------------------------------------------")
print ("Entered incorrectly to many times.....Exiting")
print ("----------------------------------------------------")
break
else:
if spikewell > 50:
print ("parameters past limit (20)")
print ("----------------------------------------------------")
print (tries)
continue
elif spikewell < 0:
print ("Parameters cant be negative")
print ("----------------------------------------------------")
print (tries)
continue
else:
spikewell
print ("Parameters are set")
print (spikewell)
print ("Searching files")
print ("----------------------------------------------------")
for z in Blks:
df = pd.read_csv(z, sep=r'\s+', names=['X','Y','Z'])
z = sum(df['Z'])
average = z / len(df['Z'])
for terrain in Blks:
for df in terrain:
df = pd.read_csv(terrain, sep=r'\s+', names=['X','Y','Z'])
spike_zleft = df['Z'] - df['Z'].shift(1)
spike_zright = df['Z'] - df['Z'].shift(-1)
wzdown = -(df['Z'] - df['Z'].shift(-1))
wzup_abs = abs(df['Z'] - df['Z'].shift(1))
wzdown_abs = abs(wzdown)
spikecsv = ('spikes.csv')
wellcsv = ('wells.csv')
spikes_search = df.loc[(spike_zleft > spikewell) & (spike_zright > spikewell)]
with open(spikecsv, 'a') as f:
spikes_search[['X','Y','Z']].to_csv(f, sep='\t', index=False)
well_search = df.loc[(wzup_abs > spikewell) & (wzdown > spikewell)]
with open(wellcsv, 'a') as f:
well_search[['X','Y','Z']].to_csv(f, sep='\t', index=False)
print ("----------------------------------------------------")
print ('Search completed')
if len(spikes_search) == 0:
print ("0 SPIKE\S FOUND")
elif len(spikes_search) > 0:
print (terrain)
print (str(len(spikes_search)) + " SPIKE\S FOUND")
elif len(spikes_search) > 0:
print (str(len(spikes_search)) + " SPIKE\S FOUND")
if len(well_search) == 0:
print ("0 WELL\S FOUND")
elif len(well_search) > 0:
print (str(len(well_search)) + " WELL\S FOUND")
elif len(well_search) > 0:
print (str(len(well_search)) + " WELL\S FOUND")
break
break
答案 0 :(得分：0)
这里有两个问题，导入数据并使用它。最好描述一下你在做什么数据，而不是提供你的整个脚本。请参阅https://stackoverflow.com/help/mcve
关于CSV输入，请使用csv模块！
import csv
with open('FILENAME','r') as f:
data = []
readr = csv.reader(f)
for line in readr:
data.append([float(i) for i in line])
但是你的熊猫代码已经真的做到了。
如果您正在使用数组进行数值处理（看起来像是这样），那么您应该查看numpy http://www.numpy.org/。这个模块，或者更确切地说是模块集合，可能已经具有了您正在尝试的功能。具体来说，您正在寻找本地最小值和最大值。
一旦你有numpy数组，你会发现其他人试图这样做： Find all local Maxima and Minima when x and y values are given as numpy arrays
此外： Get coordinates of local maxima in 2D array above certain value