我正在pandas使用matplotlib. 我有一個(gè)繪圖策略，我將歸零到初始值，然后將每個(gè)選定的變量偏移一個(gè)設(shè)定值。例如，這是我目前的繪圖方法：fig, ax = plt.subplots()# data is in a dataframe called inputDatatimeseries_plots=['var1','var3','var8']offsetFactor = 20for ii,var in enumerate(timeseries_plots) offsetRef = inputData[var].loc[~inputData[var].isnull()].iloc[0] ax.plot(inputData[TimeIndex], offsetFactor*(len(timeseries_plots_avg)-ii-1)+inputData[timeseries_plots_avg[ii]]-offsetRef, label=var,markersize=1,marker='None',linestyle = 'solid',color=colour)plt.show()這產(chǎn)生了這樣的東西（有一些matplotlib技巧）：如您所見(jiàn)，它刪除了offsetRef（在本例中為變量的初始值），然后offsetFactor向每個(gè)變量添加一個(gè)常量（在本例中等于 20）。結(jié)果是開始垂直偏移 20 的行。但是，當(dāng)值開始隨時(shí)間漂移并且一個(gè)變量可能與另一個(gè)變量交叉時(shí)，這可能會(huì)成為一個(gè)問(wèn)題。我想做的是重置垂直偏移量 - 例如通過(guò)在特定日期之后更改 offsetRef。我試圖通過(guò)以下方式做到這一點(diǎn)。我首先初始化一個(gè)等于變量大小的數(shù)組。offsetRef然后我用在 處重新計(jì)算的值填充它resetDates。我已經(jīng)包含了注釋，這些注釋標(biāo)記了#PSEUDOCODE我大致寫下我想做的事情的地方——但提前道歉，因?yàn)樗鼈兎浅４植?。先感謝您！fig, ax = plt.subplots()inputData = pd.DataFrame(np.random.randint(100, size=(100, 5)), columns=['timestamp','var2','var3','var4','var5'])inputData['timestamp'][:]=pd.date_range('2020-may-01','2020-aug-08')timeseries_plots=['var1','var3','var4']offsetFactor = 20resetDates = ['2020-jun-23','2020-jul-05']for ii,var in enumerate(timeseries_plots) offsetRef = np.zeros(inputData[var].size) for tt,ttdate in enumerate(resetDates): if tt=0: #PSEUDO CODE: offsetRef[ inputData['timestamp'] <resetDates[tt]] = inputData[var].loc[~inputData[var].isnull()].iloc[0] #PSEUDO CODE: offsetRef[ inputData['timestamp'] >=resetDates[tt]] = inputData[var].loc[~inputData[var].isnull()].iloc[ttdate] #PSEUDO CODE: offsetRef[ inputData['timestamp'] >=resetDates[tt]] = inputData[var].loc[~inputData[var].isnull()].iloc[ttdate] ax.plot(inputData[TimeIndex], offsetFactor*(len(timeseries_plots_avg)-ii-1)+inputData[timeseries_plots_avg[ii]]-offsetRef, label=var,markersize=1,marker='None',linestyle = 'solid',color=colour)plt.show()
1 回答
當(dāng)年話下
TA貢獻(xiàn)1890條經(jīng)驗(yàn) 獲得超9個(gè)贊
這是我將堅(jiān)持的當(dāng)前解決方案，以便它可能對(duì)其他人有用：
fig, ax = plt.subplots()
# set up df
inputData = pd.DataFrame(np.random.randint(100, size=(100, 5)), columns=['timestamp','var2','var3','var4','var5'])
inputData['timestamp'][:]=pd.date_range('2020-may-01','2020-aug-08')
inputData['var2']=np.arange(0,100,1)
inputData['var2'][0:3]=49
inputData['var4']=np.arange(0,200,2)
inputData['var2'][0:3]=np.nan
# set constants and settings
dispFactor=20
timeseries_plots=['var2','var4']
resetDates=['2020-05-05','2020-05-20', '2020-08-04']
offsetFactor = dispFactor
#begin
fig, ax=plt.subplots()
for ii,var in enumerate(timeseries_plots):
offsetRef = np.zeros(inputData[var].size)
for tt,ttdate in enumerate(resetDates):
if tt==0:
if inputData[var].loc[inputData['timestamp']==ttdate].isna().bool(): #if date is nan
print('a',inputData[var].loc[~inputData[var].isnull()].iloc[0],inputData[var].bfill().loc[inputData['timestamp']==ttdate])
offsetRef[(inputData['timestamp']<ttdate)]= inputData[var].loc[~inputData[var].isnull()].iloc[0]
offsetRef[(inputData['timestamp']>=ttdate)]=inputData[var].bfill().loc[inputData['timestamp']==ttdate]
else:
print('b',inputData[var].loc[~inputData[var].isnull()].iloc[0],inputData[var].loc[inputData['timestamp']==ttdate])
offsetRef[(inputData['timestamp']<ttdate)]= inputData[var].loc[~inputData[var].isnull()].iloc[0]
offsetRef[(inputData['timestamp']>=ttdate)]= inputData[var].loc[inputData['timestamp']==ttdate]
else:
if inputData[var].loc[inputData['timestamp']==ttdate].isna().bool(): #if date is nan
print('c')
offsetRef[ inputData['timestamp'] >=resetDates[tt]] = inputData[var].bfill().loc[inputData['timestamp']==ttdate]
else:
print('d',inputData[var].loc[inputData['timestamp']==ttdate])
offsetRef[ inputData['timestamp'] >=resetDates[tt]] = inputData[var].loc[inputData['timestamp']==ttdate]
print(offsetRef)
ax.plot(inputData['timestamp'], offsetFactor*(len(timeseries_plots)-ii-1)+inputData[var]-offsetRef)
plt.show()
這會(huì)將所選位置的偏移量“重置”為 20，resetDates以生成下圖：
在任何一種情況下，我可能都不需要 nan 數(shù)據(jù)的 if 邏輯捕獲（并且只依賴.bfill()）來(lái)工作 - 但這讓我覺(jué)得它更安全。我將在改進(jìn)解決方案時(shí)進(jìn)行編輯。
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