绘制csv数据看起来与从xml解析的相同数据不同

问题描述 投票:1回答:1

我编写了一个解析器来直接绘制gpx文件-用转换后的gpx文件替换了我以前对read_csv()的使用。确保我检查了列表和数据框read_csv()并使用我的解析函数对其进行了重建。但是由于某些原因,我的情节完全不同。 figures

我通过导出到csv再次检查了解析的数据,并用旧代码重新导入,所以我很确定我的问题不是解析器。但是那又怎样呢?没有错误消息,我无能为力。我用下面的示例数据和打印内容制作了两个直观的工作示例:

编辑:我稍微整理了一下代码,并添加了一张没有网格的打印图片,表明该轴也打印得很奇怪。single plot

旧read_csv版本的代码:

import xml.etree.cElementTree as et
from glob import glob

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

filenames = glob('data/*.csv')
file_num = len(filenames)

# create grid
Rows_count = int(np.sqrt(file_num))
Cols_count = int(file_num / Rows_count) + 1
grid_layout = [divmod(x, Rows_count) for x in range(file_num)]

# parse and fill df
df_list = [pd.read_csv(f) for f in filenames]

# plot
fig, ax = plt.subplots(Cols_count, Rows_count)
for i in range(file_num):
    ax[grid_layout[i]].plot(df_list[i]['longitude'],
                        df_list[i]['latitude'], color='k')
print(df_list[0])
print(f"df_list is a {type(df_list)} made up of { file_num }x {type(df_list[i]) }")

plt.show()

与解析器相同:

import xml.etree.cElementTree as etree
from glob import glob

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

filenames = glob('data/*.gpx')
file_num = len(filenames)
df_cols = ["lat", "lon"]
min_files = 4


def strip_ns(tag_elem):  # temporary brute force to strip namespaces
    idx = k = tag_elem.rfind("}")
    if idx != -1:
        tag_elem = tag_elem[idx + 1:]
    return tag_elem


def parse_xml(xml_file, df_cols):  # chose iterparse for large filesize data later
    rows = []
    for event, elem in etree.iterparse(xml_file, events=('start', 'end')):
        tag_names = strip_ns(elem.tag)
        if event == 'start':
            if tag_names == 'trkpt':
                res = []
                res.append(elem.attrib[df_cols[0]])
                res.append(elem.attrib[df_cols[1]])
                rows.append({df_cols[i]: res[i]
                             for i, _ in enumerate(df_cols)})
    elem.clear()
    gpx_df = pd.DataFrame(rows, columns=df_cols)
    return gpx_df


# create list of dataframes containing the tracks
df_list = [parse_xml(filenames[index], df_cols)
           for index in range(file_num)]
# create grid for plots
rows_count = int(np.sqrt(file_num))
cols_count = int(file_num / rows_count) + 1
grid_layout = [divmod(x, rows_count) for x in range(file_num)]
# plot data
fig, ax = plt.subplots(cols_count, rows_count)
for i in range(file_num):
    ax[grid_layout[i]].plot(df_list[i]['lat'], df_list[i]['lon'])
# when i export the parsed data and use it with the from_csv it displays correctly
df_list[0].to_csv('out.csv', index=False)
print(df_list[0])
# showing types
print(
    f"df_list is a {type(df_list)} made up of { file_num }x {type(df_list[i]) }")

plt.show()

样本数据(csv)-刚刚在/ data子目录中复制了5次:

date,elapsed_time,latitude,longitude,elevation
2016-10-24 12:20:14,33,41.3796800,2.1482880,36.7
2016-10-24 12:20:17,36,41.3794990,2.1485300,35.1
2016-10-24 12:20:18,37,41.3794560,2.1485860,34.8
2016-10-24 12:20:19,38,41.3794260,2.1486260,34.5
2016-10-24 12:20:20,39,41.3794020,2.1486590,34.3
2016-10-24 12:20:21,40,41.3793710,2.1486990,34.1
2016-10-24 12:20:22,41,41.3793370,2.1487450,33.8
2016-10-24 12:20:23,42,41.3793040,2.1487890,33.8
2016-10-24 12:20:24,43,41.3792640,2.1488420,33.7
2016-10-24 12:20:25,44,41.3792230,2.1488960,33.6
2016-10-24 12:20:26,45,41.3791800,2.1489530,33.4
2016-10-24 12:20:27,46,41.3791440,2.1490020,33.3
2016-10-24 12:20:28,47,41.3791130,2.1490440,33.2
2016-10-24 12:20:29,48,41.3790650,2.1491070,33.0
2016-10-24 12:20:30,49,41.3790290,2.1491540,32.8
2016-10-24 12:20:31,50,41.3789850,2.1492140,32.6
2016-10-24 12:20:32,51,41.3789390,2.1492740,32.3
2016-10-24 12:20:33,52,41.3788890,2.1493400,32.0
2016-10-24 12:20:34,53,41.3788440,2.1494000,31.7
2016-10-24 12:20:38,57,41.3786540,2.1496550,31.0
2016-10-24 12:20:39,58,41.3786000,2.1497270,30.9
2016-10-24 12:20:40,59,41.3785640,2.1497750,30.9
2016-10-24 12:20:41,60,41.3785150,2.1498400,30.9
2016-10-24 12:20:42,61,41.3784850,2.1498810,30.9
2016-10-24 12:20:43,62,41.3784480,2.1499290,31.0
2016-10-24 12:20:44,63,41.3784170,2.1499720,31.0
2016-10-24 12:20:45,64,41.3783760,2.1500260,31.1
2016-10-24 12:20:46,65,41.3783440,2.1500690,31.2
2016-10-24 12:20:47,66,41.3783050,2.1501210,31.3
2016-10-24 12:20:48,67,41.3782760,2.1501600,31.4
2016-10-24 12:20:49,68,41.3782500,2.1501950,31.5
2016-10-24 12:20:51,70,41.3782070,2.1502510,31.6
2016-10-24 12:20:52,71,41.3781830,2.1502840,31.7
2016-10-24 12:20:57,76,41.3781460,2.1503340,31.9
2016-10-24 12:21:3,82,41.3781190,2.1503690,32.0
2016-10-24 12:21:5,84,41.3780900,2.1504080,32.2
2016-10-24 12:21:6,85,41.3780710,2.1504340,32.4
2016-10-24 12:21:7,86,41.3780510,2.1504600,32.7
2016-10-24 12:21:8,87,41.3780310,2.1504860,33.0
2016-10-24 12:21:11,90,41.3778300,2.1503570,33.4
2016-10-24 12:21:12,91,41.3778020,2.1503200,33.5
2016-10-24 12:21:13,92,41.3777720,2.1502810,33.6
2016-10-24 12:21:14,93,41.3777290,2.1502240,33.6
2016-10-24 12:21:15,94,41.3776950,2.1501790,33.7
2016-10-24 12:21:16,95,41.3776550,2.1501250,33.7
2016-10-24 12:21:17,96,41.3776210,2.1500800,33.7
2016-10-24 12:21:18,97,41.3775850,2.1500330,33.6
2016-10-24 12:21:19,98,41.3775500,2.1499860,33.5
2016-10-24 12:21:20,99,41.3775160,2.1499410,33.5
2016-10-24 12:21:21,100,41.3774790,2.1498930,33.3
2016-10-24 12:21:22,101,41.3774420,2.1498430,33.2
2016-10-24 12:21:23,102,41.3774110,2.1498030,33.1
2016-10-24 12:21:24,103,41.3773730,2.1497530,32.9
2016-10-24 12:21:25,104,41.3773280,2.1496930,32.6
2016-10-24 12:21:26,105,41.3772800,2.1496290,32.3
2016-10-24 12:21:27,106,41.3772420,2.1495790,32.0
2016-10-24 12:21:28,107,41.3772080,2.1495340,32.2
2016-10-24 12:21:29,108,41.3771660,2.1494750,32.4
2016-10-24 12:21:30,109,41.3771310,2.1494260,32.6
2016-10-24 12:21:34,113,41.3770050,2.1492470,33.4
2016-10-24 12:21:35,114,41.3769710,2.1491990,33.7
2016-10-24 12:21:36,115,41.3769310,2.1491430,34.1
2016-10-24 12:21:37,116,41.3768910,2.1490860,34.6
2016-10-24 12:21:38,117,41.3768520,2.1490310,35.1
2016-10-24 12:21:39,118,41.3768140,2.1489770,35.7
2016-10-24 12:21:40,119,41.3767670,2.1489120,36.4
2016-10-24 12:21:41,120,41.3767340,2.1488670,37.0
2016-10-24 12:21:42,121,41.3766860,2.1488010,37.9
2016-10-24 12:21:43,122,41.3766600,2.1487660,38.4
2016-10-24 12:21:44,123,41.3766230,2.1487160,39.2
2016-10-24 12:21:45,124,41.3765980,2.1486820,39.8
2016-10-24 12:21:46,125,41.3765730,2.1486490,40.4
2016-10-24 12:21:47,126,41.3765520,2.1486220,40.8
2016-10-24 12:21:48,127,41.3765330,2.1485970,41.2
2016-10-24 12:21:50,129,41.3764970,2.1485490,42.0
2016-10-24 12:21:52,131,41.3764660,2.1485090,42.6
2016-10-24 12:21:54,133,41.3764430,2.1484780,43.0
2016-10-24 12:22:19,158,41.3764120,2.1484370,43.6
2016-10-24 12:22:20,159,41.3763870,2.1484050,44.1
2016-10-24 12:22:21,160,41.3763600,2.1483700,44.6
2016-10-24 12:22:22,161,41.3763330,2.1483340,45.0
2016-10-24 12:22:23,162,41.3763100,2.1483040,45.4
2016-10-24 12:22:24,163,41.3762590,2.1481560,47.0
2016-10-24 12:22:26,165,41.3762240,2.1481230,47.0
2016-10-24 12:22:27,166,41.3761840,2.1480860,46.9
2016-10-24 12:22:28,167,41.3761570,2.1480610,46.9
2016-10-24 12:22:29,168,41.3761310,2.1480360,46.8
2016-10-24 12:22:31,170,41.3760590,2.1479890,46.3
2016-10-24 12:22:32,171,41.3759870,2.1481790,44.6
2016-10-24 12:22:33,172,41.3759260,2.1482390,43.9
2016-10-24 12:22:34,173,41.3758990,2.1482760,43.6
2016-10-24 12:22:36,175,41.3760600,2.1485540,43.1
2016-10-24 12:22:37,176,41.3760340,2.1485900,42.8
2016-10-24 12:22:38,177,41.3760080,2.1486280,42.6
2016-10-24 12:22:39,178,41.3759780,2.1486710,42.4
2016-10-24 12:22:40,179,41.3759280,2.1487420,42.0
2016-10-24 12:22:41,180,41.3758990,2.1487840,41.6
2016-10-24 12:22:42,181,41.3758630,2.1488360,41.0
2016-10-24 12:22:43,182,41.3758350,2.1488750,40.5
2016-10-24 12:22:44,183,41.3758160,2.1489020,40.2
2016-10-24 12:22:47,186,41.3755490,2.1489390,40.0
2016-10-24 12:22:49,188,41.3754640,2.1489150,40.4
2016-10-24 12:22:50,189,41.3753500,2.1487540,41.4

样本数据(gpx)-刚在/ data子目录中复制了5次:

<?xml version="1.0" encoding="UTF-8"?>
<gpx creator="StravaGPX iPhone" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.topografix.com/GPX/1/1 http://www.topografix.com/GPX/1/1/gpx.xsd" version="1.1" xmlns="http://www.topografix.com/GPX/1/1">
 <metadata>
  <time>2016-10-24T12:19:41Z</time>
 </metadata>
 <trk>
  <name>Afternoon Ride</name>
  <type>1</type>
  <trkseg>
   <trkpt lat="41.3796800" lon="2.1482880">
    <ele>36.7</ele>
    <time>2016-10-24T12:20:14Z</time>
   </trkpt>
   <trkpt lat="41.3794990" lon="2.1485300">
    <ele>35.1</ele>
    <time>2016-10-24T12:20:17Z</time>
   </trkpt>
   <trkpt lat="41.3794560" lon="2.1485860">
    <ele>34.8</ele>
    <time>2016-10-24T12:20:18Z</time>
   </trkpt>
   <trkpt lat="41.3794260" lon="2.1486260">
    <ele>34.5</ele>
    <time>2016-10-24T12:20:19Z</time>
   </trkpt>
   <trkpt lat="41.3794020" lon="2.1486590">
    <ele>34.3</ele>
    <time>2016-10-24T12:20:20Z</time>
   </trkpt>
   <trkpt lat="41.3793710" lon="2.1486990">
    <ele>34.1</ele>
    <time>2016-10-24T12:20:21Z</time>
   </trkpt>
   <trkpt lat="41.3793370" lon="2.1487450">
    <ele>33.8</ele>
    <time>2016-10-24T12:20:22Z</time>
   </trkpt>
   <trkpt lat="41.3793040" lon="2.1487890">
    <ele>33.8</ele>
    <time>2016-10-24T12:20:23Z</time>
   </trkpt>
   <trkpt lat="41.3792640" lon="2.1488420">
    <ele>33.7</ele>
    <time>2016-10-24T12:20:24Z</time>
   </trkpt>
   <trkpt lat="41.3792230" lon="2.1488960">
    <ele>33.6</ele>
    <time>2016-10-24T12:20:25Z</time>
   </trkpt>
   <trkpt lat="41.3791800" lon="2.1489530">
    <ele>33.4</ele>
    <time>2016-10-24T12:20:26Z</time>
   </trkpt>
   <trkpt lat="41.3791440" lon="2.1490020">
    <ele>33.3</ele>
    <time>2016-10-24T12:20:27Z</time>
   </trkpt>
   <trkpt lat="41.3791130" lon="2.1490440">
    <ele>33.2</ele>
    <time>2016-10-24T12:20:28Z</time>
   </trkpt>
   <trkpt lat="41.3790650" lon="2.1491070">
    <ele>33.0</ele>
    <time>2016-10-24T12:20:29Z</time>
   </trkpt>
   <trkpt lat="41.3790290" lon="2.1491540">
    <ele>32.8</ele>
    <time>2016-10-24T12:20:30Z</time>
   </trkpt>
   <trkpt lat="41.3789850" lon="2.1492140">
    <ele>32.6</ele>
    <time>2016-10-24T12:20:31Z</time>
   </trkpt>
   <trkpt lat="41.3789390" lon="2.1492740">
    <ele>32.3</ele>
    <time>2016-10-24T12:20:32Z</time>
   </trkpt>
   <trkpt lat="41.3788890" lon="2.1493400">
    <ele>32.0</ele>
    <time>2016-10-24T12:20:33Z</time>
   </trkpt>
   <trkpt lat="41.3788440" lon="2.1494000">
    <ele>31.7</ele>
    <time>2016-10-24T12:20:34Z</time>
   </trkpt>
   <trkpt lat="41.3786540" lon="2.1496550">
    <ele>31.0</ele>
    <time>2016-10-24T12:20:38Z</time>
   </trkpt>
   <trkpt lat="41.3786000" lon="2.1497270">
    <ele>30.9</ele>
    <time>2016-10-24T12:20:39Z</time>
   </trkpt>
   <trkpt lat="41.3785640" lon="2.1497750">
    <ele>30.9</ele>
    <time>2016-10-24T12:20:40Z</time>
   </trkpt>
   <trkpt lat="41.3785150" lon="2.1498400">
    <ele>30.9</ele>
    <time>2016-10-24T12:20:41Z</time>
   </trkpt>
   <trkpt lat="41.3784850" lon="2.1498810">
    <ele>30.9</ele>
    <time>2016-10-24T12:20:42Z</time>
   </trkpt>
   <trkpt lat="41.3784480" lon="2.1499290">
    <ele>31.0</ele>
    <time>2016-10-24T12:20:43Z</time>
   </trkpt>
   <trkpt lat="41.3784170" lon="2.1499720">
    <ele>31.0</ele>
    <time>2016-10-24T12:20:44Z</time>
   </trkpt>
   <trkpt lat="41.3783760" lon="2.1500260">
    <ele>31.1</ele>
    <time>2016-10-24T12:20:45Z</time>
   </trkpt>
   <trkpt lat="41.3783440" lon="2.1500690">
    <ele>31.2</ele>
    <time>2016-10-24T12:20:46Z</time>
   </trkpt>
   <trkpt lat="41.3783050" lon="2.1501210">
    <ele>31.3</ele>
    <time>2016-10-24T12:20:47Z</time>
   </trkpt>
   <trkpt lat="41.3782760" lon="2.1501600">
    <ele>31.4</ele>
    <time>2016-10-24T12:20:48Z</time>
   </trkpt>
   <trkpt lat="41.3782500" lon="2.1501950">
    <ele>31.5</ele>
    <time>2016-10-24T12:20:49Z</time>
   </trkpt>
   <trkpt lat="41.3782070" lon="2.1502510">
    <ele>31.6</ele>
    <time>2016-10-24T12:20:51Z</time>
   </trkpt>
   <trkpt lat="41.3781830" lon="2.1502840">
    <ele>31.7</ele>
    <time>2016-10-24T12:20:52Z</time>
   </trkpt>
   <trkpt lat="41.3781460" lon="2.1503340">
    <ele>31.9</ele>
    <time>2016-10-24T12:20:57Z</time>
   </trkpt>
   <trkpt lat="41.3781190" lon="2.1503690">
    <ele>32.0</ele>
    <time>2016-10-24T12:21:03Z</time>
   </trkpt>
   <trkpt lat="41.3780900" lon="2.1504080">
    <ele>32.2</ele>
    <time>2016-10-24T12:21:05Z</time>
   </trkpt>
   <trkpt lat="41.3780710" lon="2.1504340">
    <ele>32.4</ele>
    <time>2016-10-24T12:21:06Z</time>
   </trkpt>
   <trkpt lat="41.3780510" lon="2.1504600">
    <ele>32.7</ele>
    <time>2016-10-24T12:21:07Z</time>
   </trkpt>
   <trkpt lat="41.3780310" lon="2.1504860">
    <ele>33.0</ele>
    <time>2016-10-24T12:21:08Z</time>
   </trkpt>
   <trkpt lat="41.3778300" lon="2.1503570">
    <ele>33.4</ele>
    <time>2016-10-24T12:21:11Z</time>
   </trkpt>
   <trkpt lat="41.3778020" lon="2.1503200">
    <ele>33.5</ele>
    <time>2016-10-24T12:21:12Z</time>
   </trkpt>
   <trkpt lat="41.3777720" lon="2.1502810">
    <ele>33.6</ele>
    <time>2016-10-24T12:21:13Z</time>
   </trkpt>
   <trkpt lat="41.3777290" lon="2.1502240">
    <ele>33.6</ele>
    <time>2016-10-24T12:21:14Z</time>
   </trkpt>
   <trkpt lat="41.3776950" lon="2.1501790">
    <ele>33.7</ele>
    <time>2016-10-24T12:21:15Z</time>
   </trkpt>
   <trkpt lat="41.3776550" lon="2.1501250">
    <ele>33.7</ele>
    <time>2016-10-24T12:21:16Z</time>
   </trkpt>
   <trkpt lat="41.3776210" lon="2.1500800">
    <ele>33.7</ele>
    <time>2016-10-24T12:21:17Z</time>
   </trkpt>
   <trkpt lat="41.3775850" lon="2.1500330">
    <ele>33.6</ele>
    <time>2016-10-24T12:21:18Z</time>
   </trkpt>
   <trkpt lat="41.3775500" lon="2.1499860">
    <ele>33.5</ele>
    <time>2016-10-24T12:21:19Z</time>
   </trkpt>
   <trkpt lat="41.3775160" lon="2.1499410">
    <ele>33.5</ele>
    <time>2016-10-24T12:21:20Z</time>
   </trkpt>
   <trkpt lat="41.3774790" lon="2.1498930">
    <ele>33.3</ele>
    <time>2016-10-24T12:21:21Z</time>
   </trkpt>
   <trkpt lat="41.3774420" lon="2.1498430">
    <ele>33.2</ele>
    <time>2016-10-24T12:21:22Z</time>
   </trkpt>
   <trkpt lat="41.3774110" lon="2.1498030">
    <ele>33.1</ele>
    <time>2016-10-24T12:21:23Z</time>
   </trkpt>
   <trkpt lat="41.3773730" lon="2.1497530">
    <ele>32.9</ele>
    <time>2016-10-24T12:21:24Z</time>
   </trkpt>
   <trkpt lat="41.3773280" lon="2.1496930">
    <ele>32.6</ele>
    <time>2016-10-24T12:21:25Z</time>
   </trkpt>
   <trkpt lat="41.3772800" lon="2.1496290">
    <ele>32.3</ele>
    <time>2016-10-24T12:21:26Z</time>
   </trkpt>
   <trkpt lat="41.3772420" lon="2.1495790">
    <ele>32.0</ele>
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python pandas numpy matplotlib
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