Question

我不久前发现了大熊猫，看起来非常有趣。到目前为止，我通常在Python中使用结构化掩码数组，似乎熊猫可以提供替代方案，特别是因为我经常使用时间序列。但是，在每个时刻，我都有一些标量和一些数组数据：标量时间，标量纬度，标量经度，以及矢量压力和矢量轮廓：

dtype([('lat', '<f4'), ('ch4_profile', '<f4', (110,)), ('time', '<M8[us]'), ('lon', '<f4'), ('p', '<f4', (110,))])

我会使用数据进行分析，例如查找与其他数据集的匹配，以及可视化，例如单独可视化配置文件，或者在地图上显示每个配置文件的某些统计信息。

如果我理解正确，熊猫＆＃39; DataFrame 是二维的，而 Panel 是三维的。是否有任何类似的熊猫来描述所指示的数据？或者我可以更好地坚持（更灵活？）标准ndarray（现在）？

编辑：示例数据

根据评论，这里有一些示例数据，适合复制粘贴到代码中（请注意，在现实世界中，我使用的是numpy.ma.MaskedArray）。

array([ (70.00604248046875, [1.8543829917907715, 1.8604300022125244, 1.8616620302200317, 1.8601950407028198, 1.851915955543518, 1.8489810228347778, 1.8222110271453857, 1.8655049800872803, 1.7066600322723389, 1.687608003616333, 1.2694480419158936, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0], datetime.datetime(2010, 3, 23, 2, 24, 54, 210000), -52.05939865112305, [863.52294921875, 669.9515380859375, 583.3341064453125, 507.91802978515625, 442.25689697265625, 385.1185607910156, 335.3567199707031, 292.0868225097656, 246.5142822265625, 183.48988342285156, 72.71968078613281, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0]),
       (83.92768859863281, [1.8359440565109253, 1.8322449922561646, 1.834841012954712, 1.824031949043274, 1.8172659873962402, 1.8016170263290405, 1.8238199949264526, 1.6459150314331055, 1.6390000581741333, 1.2601679563522339, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0], datetime.datetime(2010, 3, 30, 21, 3, 12, 478000), -66.69522094726562, [850.73779296875, 612.4067993164062, 513.5870361328125, 447.0830993652344, 389.2227478027344, 338.8443298339844, 295.01129150390625, 248.8583984375, 190.45912170410156, 78.76023864746094, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0])], 
      dtype=[('lat', '<f4'), ('ch4_profile', '<f4', (110,)), ('time', '<M8[us]'), ('lon', '<f4'), ('p', '<f4', (110,))])

Answer 1

我将您的数据压平并将其投入数据框：

import numpy as np
import datetime
import pandas

x = np.array([ 
    (70.00604248046875, [1.8543829917907715, 1.8604300022125244, 1.8616620302200317, 1.8601950407028198, 1.851915955543518, 1.8489810228347778, 1.8222110271453857, 1.8655049800872803, 1.7066600322723389, 1.687608003616333, 1.2694480419158936, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0], datetime.datetime(2010, 3, 23, 2, 24, 54, 210000), -52.05939865112305, [863.52294921875, 669.9515380859375, 583.3341064453125, 507.91802978515625, 442.25689697265625, 385.1185607910156, 335.3567199707031, 292.0868225097656, 246.5142822265625, 183.48988342285156, 72.71968078613281, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0]),
    (83.92768859863281, [1.8359440565109253, 1.8322449922561646, 1.834841012954712, 1.824031949043274, 1.8172659873962402, 1.8016170263290405, 1.8238199949264526, 1.6459150314331055, 1.6390000581741333, 1.2601679563522339, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0], datetime.datetime(2010, 3, 30, 21, 3, 12, 478000), -66.69522094726562, [850.73779296875, 612.4067993164062, 513.5870361328125, 447.0830993652344, 389.2227478027344, 338.8443298339844, 295.01129150390625, 248.8583984375, 190.45912170410156, 78.76023864746094, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0])
    ], 
    dtype=[('lat', '<f4'), ('ch4', '<f4', (110,)), ('time', '<M8[us]'), ('lon', '<f4'), ('p', '<f4', (110,))]
)

finallist = []
for n, row in enumerate(x):
    for m, (p, ch4) in enumerate(zip(row['p'], row['ch4'])): 
        outputrow = [n, m]
        for ic in ['lat', 'lon', 'time']:
            outputrow.append(row[ic])
        outputrow.extend([p, ch4])
        finallist.append(outputrow)

df = pandas.DataFrame(finallist, columns=['series', 'measurement', 'lat', 'lon', 'time', 'p', 'ch4'])
#df.set_index(['series', 'measurement'], inplace=True)
df['p'][df['p'] < 0] = np.nan
df['ch4'][df['ch4'] < 0] = np.nan

print(df.head().to_string())

那么：

df.head()

   series  measurement        lat        lon                       time           p       ch4
0       0            0  70.006042 -52.059399 2010-03-23 02:24:54.210000  863.522949  1.854383
1       0            1  70.006042 -52.059399 2010-03-23 02:24:54.210000  669.951538  1.860430
2       0            2  70.006042 -52.059399 2010-03-23 02:24:54.210000  583.334106  1.861662
3       0            3  70.006042 -52.059399 2010-03-23 02:24:54.210000  507.918030  1.860195
4       0            4  70.006042 -52.059399 2010-03-23 02:24:54.210000  442.256897  1.851916

和

df.tail()
     series  measurement        lat        lon                       time   p  ch4
215       1          105  83.927689 -66.695221 2010-03-30 21:03:12.478000 NaN  NaN
216       1          106  83.927689 -66.695221 2010-03-30 21:03:12.478000 NaN  NaN
217       1          107  83.927689 -66.695221 2010-03-30 21:03:12.478000 NaN  NaN
218       1          108  83.927689 -66.695221 2010-03-30 21:03:12.478000 NaN  NaN
219       1          109  83.927689 -66.695221 2010-03-30 21:03:12.478000 NaN  NaN

那么：

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df.plot(x='ch4', y='p',  use_index=True, subplots=True, ax=ax, legend=True)
ax.set_ylabel(r'$p$')
ax.set_xlabel(r'$\mathrm{CH}_4$')

给出：

enter image description here

或者如果你可以安装seaborn（强烈推荐）：

seaborn.lmplot('ch4', 'p', data=df, hue='series', fit_reg=False)

给出

enter image description here

在熊猫中描述时间序列？

1 个答案: