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我有一个时间序列的数据框，其中列是时间值（按顺序），每一行都是一个单独的系列。我还有额外的列来给出每一行的类别，这反过来又决定了线型和颜色。

这是数据框：

>>> df
                cat (frac_norm, 2, 1)                                                                                                       cluster
month_rel                           -5        -4        -3        -2        -1         0         1          2         3         4          5        
user1   user2                                                                                                                                       
3414845 4232621  -1b          0.760675  0.789854   0.95941  0.867755  0.790102         1  0.588729   0.719073  0.695572  0.647696   0.656323       4
4369232 3370279  -1b          0.580436  0.546761   0.71343  0.742033  0.802198  0.389957  0.861451   0.651786  0.798265  0.476305   0.896072       0
22771   3795428  -1b          0.946188  0.499531  0.834885  0.825772  0.754018   0.67823  0.430692   0.353989  0.333761  0.284759   0.260501       2
2660226 3126314  -1b          0.826701   0.81203  0.765182  0.680162  0.763475  0.802632         1   0.780186  0.844019  0.868698   0.722672       4
4154510 4348009  -1b                 1  0.955656  0.677647  0.911556   0.76613  0.743759   0.61798   0.606536  0.715528  0.614902   0.482267       3
2860801 164553   -1b          0.870056  0.371981  0.640212  0.835185  0.673108  0.536585         1   0.850242  0.551198  0.873016   0.635556       4
120577  3480468  -1b            0.8197  0.879873  0.961178         1  0.855465  0.827824  0.827139   0.304011  0.574978  0.473996   0.358934       3
6692132 5095003  -1b                 1  0.995859  0.738418  0.991217  0.854336  0.936518  0.910347   0.883205  0.987796  0.699433   0.815072       4
2515737 4263756  -1b          0.949047  0.990238  0.899524         1  0.961066   0.83703  0.835114   0.759142  0.749727  0.886913   0.936961       4
707596  2856619  -1b          0.780538  0.702179  0.568627         1  0.601382  0.789116         0  0.0714286         0  0.111969  0.0739796       2

我可以制作下面的图，其中 x 轴是 的有序值('frac_norm',2,1)，颜色取决于 的值cluster，线条样式取决于 的值cat。但是，它是逐行的。有没有办法通过使用 groupby 来对它进行矢量化？

在此处输入图像描述

我生成图像的代码

import pandas as pd
import numpy as np

colors = ['r','g','b','c','y','k']
lnst = ['-','--']
cats = np.sort(df['cat'].unique())
clusters = np.sort(df['cluster'].unique())
colordict = dict(zip(clusters, colors))
lnstdict = dict(zip(cats,lnst))

fig, ax = plt.subplots()
# I first do it by `cluster` value
for clus_val in clusters:
    clr = colordict[clus_val]
    subset = df[df['cluster'] == clus_val]
    # and then plot each row individually, setting the color and linestyle
    for row in subset.iterrows():
        ax.plot(row[1][('frac_norm', 2, 1)], color=clr,
               linestyle=lnstdict[row[1]['cat'][0]]
               )

生成df的代码

import pandas as pd
import numpy as np

vals = np.array([['-1b', 0.7606747496046389, 0.7898535589129476, 0.959409594095941,
        0.8677546569280126, 0.7901020186672455, 1.0, 0.5887286145588728,
        0.7190726452719073, 0.6955719557195572, 0.6476962793343348,
        0.6563233814156323, 4],
       ['-1b', 0.5804363905325444, 0.5467611336032389,
        0.7134300126103406, 0.7420329670329671, 0.8021978021978022,
        0.389957264957265, 0.861451048951049, 0.6517857142857143,
        0.798265460030166, 0.4763049450549451, 0.8960720130932898, 0],
       ['-1b', 0.9461875843454791, 0.49953095684803, 0.8348848603625673,
        0.8257715338553662, 0.7540183696900115, 0.6782302664655606,
        0.43069179143004643, 0.35398860398860393, 0.33376068376068374,
        0.28475935828877, 0.260501012145749, 2],
       ['-1b', 0.8267008985879333, 0.8120300751879698,
        0.7651821862348178, 0.680161943319838, 0.7634749524413443,
        0.8026315789473684, 1.0, 0.7801857585139319, 0.8440191387559809,
        0.8686980609418281, 0.7226720647773278, 4],
       ['-1b', 1.0, 0.955656108597285, 0.6776470588235294,
        0.9115556882651537, 0.766129636568003, 0.7437589670014347,
        0.6179800221975582, 0.6065359477124183, 0.715527950310559,
        0.6149019607843138, 0.4822670674109059, 3],
       ['-1b', 0.8700564971751412, 0.3719806763285024,
        0.6402116402116402, 0.8351851851851851, 0.6731078904991948,
        0.5365853658536585, 1.0, 0.8502415458937197, 0.55119825708061,
        0.873015873015873, 0.6355555555555555, 4],
       ['-1b', 0.8196997807387418, 0.879872907246731, 0.961178456344944,
        1.0, 0.8554654738607772, 0.8278240873814314, 0.8271388025408839,
        0.3040112596762843, 0.5749778172138421, 0.47399605003291634,
        0.35893441346004046, 3],
       ['-1b', 1.0, 0.9958592132505176, 0.7384176764076977,
        0.9912165129556433, 0.8543355440923606, 0.9365176566646254,
        0.9103471520053926, 0.8832054560954816, 0.9877955758962623,
        0.6994328922495274, 0.8150724637681159, 4],
       ['-1b', 0.9490474080638015, 0.9902376128200405,
        0.8995240613432046, 1.0, 0.9610655737704917, 0.837029893924783,
        0.8351136964569011, 0.759142496847415, 0.7497267759562841,
        0.8869130313976105, 0.9369612979550449, 4],
       ['-1b', 0.7805383022774327, 0.7021791767554478,
        0.5686274509803921, 1.0, 0.6013824884792627, 0.7891156462585033,
        0.0, 0.07142857142857142, 0.0, 0.11196911196911197,
        0.07397959183673469, 2]], dtype=object) 

cols = pd.MultiIndex.from_tuples([(              'cat', ''),
            (('frac_norm', 2, 1), -5),
            (('frac_norm', 2, 1), -4),
            (('frac_norm', 2, 1), -3),
            (('frac_norm', 2, 1), -2),
            (('frac_norm', 2, 1), -1),
            (('frac_norm', 2, 1),  0),
            (('frac_norm', 2, 1),  1),
            (('frac_norm', 2, 1),  2),
            (('frac_norm', 2, 1),  3),
            (('frac_norm', 2, 1),  4),
            (('frac_norm', 2, 1),  5),
            (          'cluster', '')],
           names=[None, 'month_rel'])

idx = pd.MultiIndex.from_tuples([(3414845, 4232621),
            (4369232, 3370279),
            (  22771, 3795428),
            (2660226, 3126314),
            (4154510, 4348009),
            (2860801,  164553),
            ( 120577, 3480468),
            (6692132, 5095003),
            (2515737, 4263756),
            ( 707596, 2856619)],
           names=['user1', 'user2'])

df = pd.DataFrame(vals, columns=cols, index=idx)
4

1 回答 1

1

您应该能够使用 pandas 来绘制并避免循环：

from matplotlib.colors import LinearSegmentedColormap

colors = ['r','g','b','c','y','k']
lnst = ['-','--']
cats = np.sort(df['cat'].unique())
clusters = np.sort(df['cluster'].unique())
colordict = dict(zip(clusters, colors))
lnstdict = dict(zip(cats,lnst))

# transpose data frame
df1 = df.T

# map colors from colordict to cluster
cmap = df['cluster'].map(colordict).values.tolist()
# create a custom color map and line style
lscm = LinearSegmentedColormap.from_list('color', cmap)
lstyle = df['cat'].map(lnstdict).values.tolist()

# plot with pandas 
df1.iloc[1:12].reset_index(level=0, drop=True).plot(figsize=(20,10),
                                                    colormap=lscm,
                                                    style=lstyle)

在此处输入图像描述

更新（假设您希望两者都在同一个图表上）

from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
%matplotlib inline

colors = ['r','g','b','c','y','k']
lnst = ['-','--']
cats = np.sort(df['cat'].unique())
clusters = np.sort(df['cluster'].unique())
colordict = dict(zip(clusters, colors))
lnstdict = dict(zip(cats,lnst))

# transpose data frame
df1 = df.T

# map colors from colordict to cluster
cmap = df['cluster'].map(colordict).values.tolist()
# create a custom color map and line style
lscm = LinearSegmentedColormap.from_list('color', cmap)
lstyle = df['cat'].map(lnstdict).values.tolist()

c = df.columns

# not needed for your actually dataframe
# i am just converting your sample data to numeric
for i in range(len(df.columns[1:])-1):
    df[c[i+1]] = pd.to_numeric(df[c[i+1]])

# groupby and get mean of cluster
df2 = df[c[1:]].groupby('cluster').mean()


# create sublots object from matplotlib
fig, ax = plt.subplots()
# add a twin y-axis
ax2 = ax.twiny()

# plot dataframe 1
df1.iloc[1:12].reset_index(level=0, drop=True).plot(ax=ax, figsize=(20,10),
                                                    colormap=lscm,
                                                    style=lstyle)

# create legend for ax
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='center left', borderaxespad=-20)

# subplot df2
df2.plot(ax=ax2, colormap='copper')

# create legend for ax2
handles, labels = ax2.get_legend_handles_labels()
ax2.legend(handles, labels, loc='center right', borderaxespad=-20)

在此处输入图像描述

于 2020-03-30T17:48:11.833 回答