有什么方法可以正确聚合时间序列数据以使用 matplotlib/seaborn 制作散点图？

Question

有什么方法可以正确聚合时间序列数据以使用 matplotlib/seaborn 制作散点图？

kim*_*kim 2 python datetime matplotlib pandas seaborn

我想为我的时间序列数据制作时间序列散点图，其中我的数据具有分类列，需要按组聚合以首先绘制数据，然后使用seaborn或制作散点图matplotlib。我的数据是产品销售价格时间序列数据，我想看到每个产品所有者在不同市场阈值下的价格趋势。我尝试使用pandas.pivot_table,groupby来塑造绘图数据，但无法获得我想要制作的所需绘图。

可重复数据：

这是我使用的示例产品数据；我想看到每个经销商关于不同蛋白质类型的价格趋势threshold。

我的尝试

这是我目前尝试汇总我的数据以制作绘图数据，但它没有给出我正确的绘图。我敢打赌，我聚合绘图数据的方式是不正确的。谁能指出我如何正确地获得所需的情节？

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

mydf = pd.read_csv('foo.csv')
mydf=mydf.drop(mydf.columns[0], axis=1)
mydf['expected_price'] = mydf['price']*76/mydf['threshold']

g = mydf.groupby(['dealer','protein_type'])
newdf= g.apply(lambda x: pd.Series([np.average(x['threshold'])])).unstack()

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但上述尝试不起作用，因为我想绘制每个经销商的市场购买价格在每日时间序列中protein_type不同threshold的数据。我不知道处理这个时间序列的最佳方式是什么。谁能建议我或纠正我如何做到这一点？

我也尝试pandas/pivot_table聚合我的数据，但它仍然不代表绘图数据。

pv_df= pd.pivot_table(mydf, index=['date'], columns=['dealer', 'protein_type', 'threshold'],values=['price'])
pv_df= pv_df.fillna(0)
pv_df.groupby(['dealer', 'protein_type', 'threshold'])['price'].unstack().reset_index()

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但上述尝试仍然无效。同样在我的数据中，日期不是连续的，所以我假设我可以绘制月度时间序列折线图。

我试图制作情节：

这是我制作情节的尝试：

def scatterplot(x_data, y_data, x_label, y_label, title):
    fig, ax = plt.subplots()
    ax.scatter(x_data, y_data, s = 30, color = '#539caf', alpha = 0.75)

    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)
    fig.autofmt_xdate()

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所需的输出：

我想要折线图或散点图，其中 x 轴显示每月时间序列，而 y 轴显示每个不同经销商在每月时间序列protein_type上的不同threshold价值的价格。这是我想要的可能的折线图示例：

Answer 1

Tre*_*ney 5

更新为 `threshold`

选项1

这个选项是在看到选项1的结果后实施的。
- 图中有很多无法解释的信息，它们没有清楚地呈现数据
为了清楚地呈现数据，每个图应该只包含 3 个维度的数据（例如date，values和cats），分别是一dealer、一threshold和一protein_type。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import timedelta

# read the data in and parse the date column and set threshold as a str
df = pd.read_csv('data/so_data/2020-08-03 63239708/mydf.csv', parse_dates=['date'])

# calculate expected price
df['expected_price'] = df.price*76/df.threshold

# set threshold as a category
df.threshold = df.threshold.astype('category')

# set the index
df = df.set_index(['date', 'dealer', 'protein_type', 'threshold'])

# form the dataframe into a long form
dfl = df.drop(columns=['destination', 'quantity']).stack().reset_index().rename(columns={'level_4': 'cats', 0: 'values'})

# plot
for pt in dfl.protein_type.unique():
    for t in dfl.threshold.unique():
        data = dfl[(dfl.protein_type == pt) & (dfl.threshold == t)]
        if not data.empty:
            utc = len(data.threshold.unique())
            f, axes = plt.subplots(nrows=utc, ncols= 2, figsize=(20, 4), squeeze=False)
            for j in range(utc):
                for i, d in enumerate(dfl.dealer.unique()):
                    data_d = data[data.dealer == d].sort_values(['cats', 'date']).reset_index(drop=True)
                    p = sns.scatterplot('date', 'values', data=data_d, hue='cats', ax=axes[j, i])
                    if not data_d.empty:
                        p.set_title(f'{d}\nThreshold: {t}\n{pt}')
                        p.set_xlim(data_d.date.min() - timedelta(days=60), data_d.date.max() + timedelta(days=60))
                    else:
                        p.set_title(f'{d}: No Data Available\nThreshold: {t}\n{pt}')
                    
            plt.show()

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前四个地块

选项 2

这导致 4 个单独的数字threshold作为一个category类型。
threshold必须先留为一个int供expected_price计算，然后再转换。
请注意，我的数据没有额外的未命名列，因此仍需要删除该列，以下代码中未显示。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# read the data in and parse the date column and set threshold as a str
df = pd.read_csv('data/so_data/2020-08-03 63239708/mydf.csv', parse_dates=['date'])

# calculate expected price
df['expected_price'] = df.price*76/df.threshold

# set threshold as a category
df.threshold = df.threshold.astype('category')

# set the index
df = df.set_index(['date', 'dealer', 'protein_type', 'threshold'])

# form the dataframe into a long form
dfl = df.drop(columns=['destination', 'quantity']).stack().reset_index().rename(columns={'level_4': 'cats', 0: 'values'})

# plot four plots with threshold
for d in dfl.dealer.unique():
    for pt in dfl.protein_type.unique():
        plt.figure(figsize=(13, 7))
        data = dfl[(dfl.protein_type == pt) & (dfl.dealer == d)]
        sns.lineplot('date', 'values', data=data, hue='threshold', style='cats')
        plt.yscale('log')
        plt.title(f'{d}: {pt}')
        plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)

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原件不`threshold`作为类别

我不明白你在做什么：
- newdf= g.apply(lambda x: pd.Series([np.average(x['threshold'])])).unstack()
- 我认为这不是主要问题的组成部分，即绘制数据
首先需要将dataframe转成长格式，'destination'需要丢弃
有很多维度可以绘制在单个图形上
- x='date', y='values', hue='cats',style='dealer'
- 'protein_type' 需要有单独的图
- 然而，数据重叠太多'dealer'以至于难以阅读，因此需要 4 个图。

数据框设置：

请注意，我的数据没有额外的未命名列，因此仍需要删除该列，以下代码中未显示。
使用pandas.DataFrame.stack到数据帧转换为长格式

选项1：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# read the data in
df = pd.read_csv('data/so_data/2020-08-03 63239708/mydf.csv', parse_dates=['date'])

# your calculation
df['expected_price'] = df['price']*76/df['threshold']

# set the index
df = df.set_index(['date', 'dealer', 'protein_type'])

# form the dataframe into a long form
dfl = df.drop(columns=['destination']).stack().reset_index().rename(columns={'level_3': 'cats', 0: 'values'})

# display(dfl.head())
        date            dealer protein_type            cats    values
0 2001-12-22  Alpha Food Corps      chicken       threshold     50.00
1 2001-12-22  Alpha Food Corps      chicken        quantity  39037.00
2 2001-12-22  Alpha Food Corps      chicken           price      0.50
3 2001-12-22  Alpha Food Corps      chicken  expected_price      0.76
4 2001-12-27  Alpha Food Corps         beef       threshold     85.00

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选项 2：滚动平均值

pandas.DataFrame.groupby与然后。pandas.DataFrame.rolling mean.stack

df = pd.read_csv('data/so_data/2020-08-03 63239708/mydf.csv', parse_dates=['date'])
df['expected_price'] = df['price']*76/df['threshold']
df = df.set_index('date')

# groupby aggregate rolling mean and stack
dfl = df.groupby(['dealer', 'protein_type'])[['expected_price', 'price']].rolling(7).mean().stack().reset_index().rename(columns={'level_3': 'cats', 0: 'values'})

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选项 1：两个地块

该'dealer'数据是相似的分化（价格串通的人？）

for pt in dfl.protein_type.unique():
    plt.figure(figsize=(9, 5))
    data = dfl[dfl.protein_type == pt]
    sns.lineplot('date', 'values', data=data, hue='cats', style='dealer')
    plt.xlim(datetime(2001, 11, 1), datetime(2004, 8, 1))
    plt.yscale('log')
    plt.title(pt)
    plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)

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即使只有'price'和'expected_price'，'dealer'也无法确定。

选项 2：四个地块

`seaborn.FacetGrid`

g = sns.FacetGrid(data=dfl, col='dealer', row='protein_type', hue='cats', height=5, aspect=1.5)
g.map(sns.lineplot, 'date', 'values').add_legend()
plt.yscale('log')
g.set_xticklabels(rotation=90)

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来自滚动均值的数据图

嵌套循环

这将产生一列 4 个数字，首先选择 for dealer，然后选择protein_type。
可选，交换的顺序dealer和protein

for d in dfl.dealer.unique():
    for pt in dfl.protein_type.unique():
        plt.figure(figsize=(10, 5))
        data = dfl[(dfl.protein_type == pt) & (dfl.dealer == d)]
        sns.lineplot('date', 'values', data=data, hue='cats')
        plt.xlim(datetime(2001, 11, 1), datetime(2004, 8, 1))
        plt.yscale('log')
        plt.title(f'{d}: {pt}')
        plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)

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CSV 示例：

date,dealer,threshold,quantity,price,protein_type,destination
2001-12-22,Alpha Food Corps,50,39037,0.5,chicken,UK
2001-12-27,Alpha Food Corps,85,35432,1.8,beef,UK
2001-12-29,Alpha Food Corps,50,32142,0.5,chicken,UK
2001-12-30,Alpha Food Corps,85,34516,1.8,beef,UK
2002-01-02,Alpha Food Corps,85,39930,1.8,beef,UK
2002-01-04,Alpha Food Corps,85,40709,1.8,beef,UK
2002-01-08,Alpha Food Corps,94,37641,2.2,beef,UK
2002-01-08,Alpha Food Corps,85,37545,1.8,beef,UK
2002-01-08,Alpha Food Corps,85,37564,1.8,beef,UK
2002-01-08,Alpha Food Corps,85,37607,1.8,beef,UK
2002-01-08,Alpha Food Corps,85,41706,1.8,beef,UK
2002-01-08,Alpha Food Corps,90,41628,2.1,beef,UK
2002-01-08,Alpha Food Corps,65,35720,0.9,chicken,UK
2002-01-09,Alpha Food Corps,94,1581,2.2,beef,UK
2002-01-09,Alpha Food Corps,85,11426,1.8,beef,UK
2002-01-09,Alpha Food Corps,85,37489,1.8,beef,UK
2002-01-09,Alpha Food Corps,90,15630,2.1,beef,UK
2002-01-09,Alpha Food Corps,80,3136,1.6,beef,UK
2002-01-10,Alpha Food Corps,85,41919,1.8,beef,UK
2002-01-10,Alpha Food Corps,90,39932,2.1,beef,UK
2002-01-10,Alpha Food Corps,90,41665,2.1,beef,UK
2002-01-10,Alpha Food Corps,90,41860,2.1,beef,UK
2002-01-10,Alpha Food Corps,65,39879,0.9,chicken,UK
2002-01-10,Alpha Food Corps,65,39884,0.9,chicken,UK
2002-01-11,Alpha Food Corps,90,37613,2.1,beef,UK
2002-01-12,Alpha Food Corps,90,41855,2.1,beef,UK
2002-01-13,Alpha Food Corps,90,37585,2.1,beef,UK
2002-01-15,Alpha Food Corps,85,41618,1.8,beef,UK
2002-01-15,Alpha Food Corps,85,41721,1.8,beef,UK
2002-01-15,Alpha Food Corps,85,41869,1.8,beef,UK
2002-01-15,Alpha Food Corps,85,41990,1.8,beef,UK
2002-01-15,Alpha Food Corps,90,41744,2.1,beef,UK
2002-01-15,Alpha Food Corps,90,41936,2.1,beef,UK
2002-01-15,Alpha Food Corps,65,41684,1.0,chicken,UK
2002-01-15,Alpha Food Corps,65,41776,1.0,chicken,UK
2002-01-16,Alpha Food Corps,94,35891,2.2,beef,UK
2002-01-16,Alpha Food Corps,85,39985,1.8,beef,UK
2002-01-16,Alpha Food Corps,85,41754,1.8,beef,UK
2002-01-16,Alpha Food Corps,85,41811,1.8,beef,UK
2002-01-16,Alpha Food Corps,90,39838,2.1,beef,UK
2002-01-16,Alpha Food Corps,80,3244,1.7,beef,UK
2002-01-17,Alpha Food Corps,94,22245,2.2,beef,UK
2002-01-17,Alpha Food Corps,85,5186,1.8,beef,UK
2002-01-17,Alpha Food Corps,90,2016,2.1,beef,UK
2002-01-17,Alpha Food Corps,90,40875,2.1,beef,UK
2002-01-17,Alpha Food Corps,65,41440,1.0,chicken,UK
2002-01-18,Alpha Food Corps,94,12525,2.2,beef,UK
2002-01-18,Alpha Food Corps,94,31325,2.2,beef,UK
2002-01-18,Alpha Food Corps,85,15486,1.8,beef,UK
2002-01-18,Alpha Food Corps,85,29992,1.8,beef,UK
2002-01-18,Alpha Food Corps,85,39938,1.8,beef,UK
2002-01-18,Alpha Food Corps,85,41777,1.8,beef,UK
2002-01-18,Alpha Food Corps,90,9475,2.1,beef,UK
2002-01-18,Alpha Food Corps,90,9960,2.1,beef,UK
2002-01-18,Alpha Food Corps,90,41676,2.1,beef,UK
2002-01-18,Alpha Food Corps,90,41816,2.1,beef,UK
2002-01-18,Alpha Food Corps,90,42036,2.1,beef,UK
2002-01-18,Alpha Food Corps,65,41673,1.0,chicken,UK
2002-01-19,Alpha Food Corps,85,19961,1.8,beef,UK
2002-01-19,Alpha Food Corps,90,19955,2.1,beef,UK
2002-01-19,Alpha Food Corps,90,40437,2.1,beef,UK
2002-01-19,Alpha Food Corps,65,41574,1.0,chicken,UK
2002-01-19,Alpha Food Corps,65,41700,1.0,chicken,UK
2002-01-20,Alpha Food Corps,94,23278,2.2,beef,UK
2002-01-20,Alpha Food Corps,85,9230,1.8,beef,UK
2002-01-20,Alpha Food Corps,85,38842,1.8,beef,UK
2002-01-20,Alpha Food Corps,90,9173,2.1,beef,UK
2002-01-20,Alpha Food Corps,90,38608,2.1,beef,UK
2002-01-20,Alpha Food Corps,50,39191,0.8,chicken,UK
2002-01-22,Alpha Food Corps,94,41741,2.2,beef,UK
2002-01-22,Alpha Food Corps,85,39879,1.8,beef,UK
2002-01-22,Alpha Food Corps,85,41683,1.8,beef,UK
2002-01-22,Alpha Food Corps,85,41958,1.8,beef,UK
2002-01-22,Alpha Food Corps,90,41833,2.1,beef,UK
2002-01-23,Alpha Food Corps,94,20294,2.2,beef,UK
2002-01-23,Alpha Food Corps,85,15553,1.8,beef,UK
2002-01-23,Alpha Food Corps,85,40753,1.8,beef,UK
2002-01-23,Alpha Food Corps,85,41740,1.8,beef,UK
2002-01-23,Alpha Food Corps,90,1892,2.1,beef,UK
2002-01-23,Alpha Food Corps,90,39850,2.1,beef,UK
2002-01-23,Alpha Food Corps,80,3231,1.7,beef,UK
2002-01-23,Alpha Food Corps,65,41415,1.1,chicken,UK
2002-01-24,Alpha Food Corps,90,35473,2.1,beef,UK
2002-01-24,Alpha Food Corps,90,41824,2.1,beef,UK
2002-01-24,Alpha Food Corps,65,41721,1.1,chicken,UK
2002-01-25,Alpha Food Corps,85,19983,1.8,beef,UK
2002-01-25,Alpha Food Corps,85,35823,1.8,beef,UK
2002-01-25,Alpha Food Corps,90,19949,2.1,beef,UK
2002-01-25,Alpha Food Corps,90,41800,2.1,beef,UK
2002-01-25,Alpha Food Corps,65,40990,1.1,chicken,UK
2002-01-26,Alpha Food Corps,90,39938,2.1,beef,UK
2002-01-26,Alpha Food Corps,90,40641,2.1,beef,UK
2002-01-26,Alpha Food Corps,90,41550,2.1,beef,UK
2002-01-27,Alpha Food Corps,94,16589,2.2,beef,UK
2002-01-27,Alpha Food Corps,85,11669,1.8,beef,UK
2002-01-27,Alpha Food Corps,90,24982,2.1,beef,UK
2002-01-27,Alpha Food Corps,65,29819,1.1,chicken,UK
2002-01-29,Alpha Food Corps,94,37516,2.2,beef,UK
2002-01-29,Alpha Food Corps,85,37378,1.8,beef,UK
2002-01-29,Alpha Food Corps,85,37535,1.8,beef,UK
2002-01-29,Alpha Food Corps,85,40174,1.8,beef,UK
2002-01-29,Alpha Food Corps,90,37831,2.1,beef,UK
2002-01-30,Alpha Food Corps,94,34435,2.2,beef,UK
2002-01-30,Alpha Food Corps,94,39640,2.2,beef,UK
2002-01-30,Alpha Food Corps,85,1619,1.8,beef,UK
2002-01-30,Alpha Food Corps,85,3058,1.8,beef,UK
2002-01-30,Alpha Food Corps,85,20929,1.8,beef,UK
2002-01-30,Alpha Food Corps,90,3641,2.1,beef,UK
2002-01-30,Alpha Food Corps,90,20974,2.1,beef,UK
2002-01-30,Alpha Food Corps,90,31160,2.1,beef,UK
2002-01-30,Alpha Food Corps,92,38189,2.3,beef,UK
2002-01-31,Alpha Food Corps,94,8804,2.2,beef,UK
2002-01-31,Alpha Food Corps,85,17398,1.8,beef,UK
2002-01-31,Alpha Food Corps,90,13963,2.1,beef,UK
2002-01-31,Alpha Food Corps,90,37673,2.1,beef,UK
2002-01-31,Alpha Food Corps,90,40330,2.1,beef,UK
2002-01-31,Alpha Food Corps,90,40511,2.2,beef,UK
2002-01-31,Alpha Food Corps,80,38290,1.9,beef,UK
2002-01-31,Alpha Food Corps,92,37193,2.3,beef,UK
2002-02-01,Alpha Food Corps,94,5011,2.2,beef,UK
2002-02-01,Alpha Food Corps,85,18783,1.8,beef,UK
2002-02-01,Alpha Food Corps,85,41827,1.8,beef,UK
2002-02-01,Alpha Food Corps,90,16394,2.1,beef,UK
2002-02-01,Alpha Food Corps,90,23013,2.1,beef,UK
2002-02-01,Alpha Food Corps,90,39923,2.1,beef,UK
2002-02-01,Alpha Food Corps,90,41417,2.1,beef,UK
2002-02-01,Alpha Food Corps,80,15592,1.7,beef,UK
2002-02-01,Alpha Food Corps,80,38364,1.9,beef,UK
2002-02-01,Alpha Food Corps,92,37605,2.3,beef,UK
2002-02-01,Alpha Food Corps,92,39234,2.3,beef,UK
2002-02-02,Alpha Food Corps,90,34578,2.1,beef,UK
2002-02-02,Alpha Food Corps,90,41661,2.1,beef,UK
2002-02-02,Alpha Food Corps,80,3157,1.7,beef,UK
2002-02-02,Alpha Food Corps,65,41272,1.2,chicken,UK
2002-02-02,Alpha Food Corps,65,41503,1.2,chicken,UK
2002-02-02,Alpha Food Corps,92,36207,2.3,beef,UK
2002-02-05,Alpha Food Corps,94,41559,2.2,beef,UK
2002-02-05,Alpha Food Corps,85,41549,1.8,beef,UK
2002-02-05,Alpha Food Corps,85,41753,1.8,beef,UK
2002-02-05,Alpha Food Corps,85,41908,1.8,beef,UK
2002-02-05,Alpha Food Corps,90,39813,2.1,beef,UK
2002-02-05,Alpha Food Corps,90,41526,2.1,beef,UK
2002-02-05,German Food Corps,80,36031,1.9,beef,UK
2002-02-05,German Food Corps,50,38538,0.9,chicken,UK
2002-02-05,Alpha Food Corps,50,38772,0.9,chicken,UK
2002-02-05,German Food Corps,50,39099,0.9,chicken,UK
2002-02-05,German Food Corps,50,39132,0.9,chicken,UK
2002-02-05,German Food Corps,50,39207,0.9,chicken,UK
2002-02-06,Alpha Food Corps,85,41947,1.8,beef,UK
2002-02-06,German Food Corps,80,37287,1.9,beef,UK
2002-02-06,Alpha Food Corps,89,43201,2.1,beef,UK
2002-02-06,German Food Corps,50,38553,0.9,chicken,UK
2002-02-06,German Food Corps,50,38837,0.9,chicken,UK
2002-02-06,Alpha Food Corps,50,38985,0.9,chicken,UK
2002-02-06,German Food Corps,65,40386,1.4,chicken,UK
2002-02-06,Alpha Food Corps,65,41851,1.2,chicken,UK
2002-02-06,Alpha Food Corps,92,38405,2.3,beef,UK
2002-02-06,German Food Corps,73,37731,1.5,chicken,UK
2002-02-07,Alpha Food Corps,85,41097,1.9,beef,UK
2002-02-07,Alpha Food Corps,90,39582,2.1,beef,UK
2002-02-07,German Food Corps,65,38832,1.4,chicken,UK
2002-02-07,German Food Corps,50,39269,0.9,chicken,UK
2002-02-07,German Food Corps,50,40129,0.9,chicken,UK
2002-02-07,German Food Corps,50,41124,0.8,chicken,UK
2002-02-07,German Food Corps,65,41739,1.2,chicken,UK
2002-02-08,Alpha Food Corps,85,20034,1.8,beef,UK
2002-02-08,German Food Corps,85,33503,1.9,beef,UK
2002-02-08,German Food Corps,85,40780,1.9,beef,UK
2002-02-08,Alpha Food Corps,90,19913,2.1,beef,UK
2002-02-08,Alpha Food Corps,90,36682,2.1,beef,UK
2002-02-08,Alpha Food Corps,90,41624,2.1,beef,UK
2002-02-08,German Food Corps,65,37503,1.4,chicken,UK
2002-02-08,German Food Corps,50,38973,0.9,chicken,UK
2002-02-08,German Food Corps,50,39069,0.9,chicken,UK
2002-02-08,German Food Corps,50,40697,0.9,chicken,UK
2002-02-08,German Food Corps,92,36103,2.3,beef,UK
2002-02-08,Alpha Food Corps,92,38278,2.3,beef,UK
2002-02-09,Alpha Food Corps,90,39842,2.1,beef,UK
2002-02-09,Alpha Food Corps,90,16553,2.3,beef,UK
2002-02-09,Alpha Food Corps,80,18739,1.9,beef,UK
2002-02-09,German Food Corps,80,36349,1.9,beef,UK
2002-02-09,German Food Corps,65,35238,1.4,chicken,UK
2002-02-09,German Food Corps,50,38391,0.9,chicken,UK
2002-02-09,Alpha Food Corps,50,38819,0.9,chicken,UK
2002-02-09,German Food Corps,50,41691,0.9,chicken,UK
2002-02-09,Alpha Food Corps,92,40245,2.3,beef,UK
2002-02-09,German Food Corps,73,37323,1.5,chicken,UK
2002-02-09,German Food Corps,90,40312,2.2,beef,UK
2002-02-10,Alpha Food Corps,90,42108,2.1,beef,UK
2002-02-10,German Food Corps,65,37831,1.4,chicken,UK
2002-02-11,Alpha Food Corps,50,38591,0.9,chicken,UK
2002-02-12,Alpha Food Corps,94,41559,2.3,beef,UK
2002-02-12,Alpha Food Corps,85,40968,1.8,beef,UK
2002-02-12,Alpha Food Corps,85,41985,1.8,beef,UK
2002-02-12,German Food Corps,50,38931,0.9,chicken,UK
2002-02-12,German Food Corps,50,38986,0.9,chicken,UK
2002-02-12,German Food Corps,92,39684,2.3,beef,UK
2002-02-12,German Food Corps,73,36619,1.5,chicken,UK
2002-02-13,Alpha Food Corps,85,41291,1.8,beef,UK
2002-02-13,Alpha Food Corps,85,41892,1.8,beef,UK

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归档时间：	5 年，3 月前
查看次数：	557 次
最近记录：	5 年，3 月前

有什么方法可以正确聚合时间序列数据以使用 matplotlib/seaborn 制作散点图？

更新为 threshold

选项1

前四个地块

选项 2

原件不threshold作为类别

数据框设置：

选项1：

选项 2：滚动平均值

选项 1：两个地块

选项 2：四个地块

seaborn.FacetGrid

嵌套循环

CSV 示例：

更新为 `threshold`

原件不`threshold`作为类别

`seaborn.FacetGrid`