同時遍歷多個數據幀

Question

我有三個用戶的三個數據幀，具有相同的列名稱，如時間，羅盤數據，加速計數據，陀螺儀數據和相機平移信息。我想同時遍歷所有數據幀以檢查特定時間哪個用戶執行了攝像機平移並返回用戶（例如，在特定時間內已經檢測到數據幀平移）。我曾嘗試使用破折號來獲得並行性，但徒勞無益。下面是我的代碼

import pandas as pd 
import glob 
import numpy as np 
import math 
from scipy.signal import butter, lfilter 
order=3 
fs=30 
cutoff=4.0 

data=[] 
gx=[] 
gy=[] 
g_x2=[] 
g_y2=[] 


dataList = glob.glob(r'C:\Users\chaitanya\Desktop\Thesis\*.csv') 
for csv in dataList: 
     data.append(pd.read_csv(csv)) 
for i in range(0, len(data)): 
    data[i] = data[i].groupby("Time").agg(lambda x: x.value_counts().index[0]) 
    data[i].reset_index(level=0, inplace=True) 

def butter_lowpass(cutoff,fs,order=5): 
    nyq=0.5 * fs 
    nor=cutoff/nyq 
    b,a=butter(order,nor,btype='low', analog=False) 
return b,a 
def lowpass_filter(data,cutoff,fs,order=5): 
    b,a=butter_lowpass(cutoff,fs,order=order) 
    y=lfilter(b,a,data) 
return y 

for i in range(0,len(data)): 
    gx.append(lowpass_filter(data[i]["Gyro_X"],cutoff,fs,order)) 
    gy.append(lowpass_filter(data[i]["Gyro_Y"],cutoff,fs,order)) 

    g_x2.append(gx[i]*gx[i]) 
    g_y2.append(gy[i]*gy[i]) 


g_rad=[[] for _ in range(len(data))] 
g_ang=[[] for _ in range(len(data))] 

for i in range(0,len(data)): 
    for j in range(0,len(data[i])): 
      g_ang[i].append(math.degrees(math.atan(gy[i][j]/gx[i][j]))) 


    data[i]["Ang"]=g_ang[i] 


panning=[[] for _ in range(len(data))] 
for i in range(0,len(data)): 
    for j in data[i]["Ang"]: 
     if 0-30<=j<=0+30: 
      panning[i].append("Panning") 
     elif 180-30<=j<=180+30: 
      panning[i].append("left") 
     else: 
      panning[i].append("None") 
    data[i]["Panning"]=panning[i] 
result=[[] for _ in range(len(data))] 
for i in range (0,len(data)): 
    result[i].append(data[i].loc[data[i]['Panning']=='Panning','Ang']) 

Answer 1

我打算假設你想要在時間上同時遍歷。無論如何，你都希望你的三個數據框在你想要遍歷的維度中有一個索引。

我會生成3個數據幀，並在9秒內產生表示隨機秒數的行。

然後，我會將它們與pd.concat和ffill對齊，以便能夠引用任何間隙的最後已知數據。

seconds = pd.date_range('2016-08-31', periods=10, freq='S') 

n = 6 
ssec = seconds.to_series() 
sidx = ssec.sample(n).index 

df1 = pd.DataFrame(np.random.randint(1, 10, (n, 3)), 
        ssec.sample(n).index.sort_values(), 
        ['compass', 'accel', 'gyro']) 

df2 = pd.DataFrame(np.random.randint(1, 10, (n, 3)), 
        ssec.sample(n).index.sort_values(), 
        ['compass', 'accel', 'gyro']) 

df3 = pd.DataFrame(np.random.randint(1, 10, (n, 3)), 
        ssec.sample(n).index.sort_values(), 
        ['compass', 'accel', 'gyro']) 

df4 = pd.concat([df1, df2, df3], axis=1, keys=['df1', 'df2', 'df3']).ffill() 
df4 

，那麼你可以繼續通過iterrows()

for tstamp, row in df4.iterrows(): 
    print tstamp