Python pandas resampling

Question

I have the following dataframe:

    Timestamp    S_time1   S_time2   End_Time_1   End_time_2   Sign_1   Sign_2
0    2413044       0        0           0              


        

Answer 1

Input:

print df
  Timestamp  S_time1  S_time2  End_Time_1  End_time_2 Sign_1 Sign_2
0    2413044        0        0           0           0      x      x
1    2422476        0        0           0           0      x      x
2    2431908        0        0           0           0      x      x
3    2441341        0        0           0           0      x      x
4    2541232  2526631  2528631     2520631     2530631     10     80
5    2560273  2544946  2546496     2546496     2548496     40     80
6    2577224  2564010  2566010     2566010     2568010   null   null
7    2592905  2580959  2582959     2582959     2584959   null   null

2 approaches:

In [231]: %timeit s(df)
1 loops, best of 3: 2.78 s per loop

In [232]: %timeit m(df)
1 loops, best of 3: 690 ms per loop

def m(df):
    #resample column Timestamp by 100ms, convert bak to integers 
    df['Timestamp'] = df['Timestamp'].astype('timedelta64[ms]')
    df['i'] = 1
    df = df.set_index('Timestamp')
    df1 = df[[]].resample('100ms', how='first').reset_index()
    df1['Timestamp'] = (df1['Timestamp'] / np.timedelta64(1, 'ms')).astype(int)
    #felper column i for merging
    df1['i'] = 1
    #print df1

    out = df1.merge(df,on='i', how='left')
    out1 = out[['Timestamp', 'Sign_1']][(out.Timestamp >= out.S_time1) & (out.Timestamp <= out.End_Time_1)]
    out2 = out[['Timestamp', 'Sign_2']][(out.Timestamp >= out.S_time2) & (out.Timestamp <= out.End_time_2)]

    out1 = out1.rename(columns={'Sign_1':'Bin_time'})
    out2 = out2.rename(columns={'Sign_2':'Bin_time'})

    df = pd.concat([out1, out2], ignore_index=True).drop_duplicates(subset='Timestamp')
    df1 = df1.set_index('Timestamp')
    df = df.set_index('Timestamp')
    df = df.reindex(df1.index).reset_index()
    #print df.head(10)

def s(df):
    #resample column Timestamp by 100ms, convert bak to integers 
    df['Timestamp'] = df['Timestamp'].astype('timedelta64[ms]')
    df = df.set_index('Timestamp')
    out = df[[]].resample('100ms', how='first')
    out = out.reset_index()
    out['Timestamp'] = (out['Timestamp'] / np.timedelta64(1, 'ms')).astype(int)
    #print out.head(10)

    #search start end 
    def search(x):
        mask1 = (df.S_time1<=x['Timestamp']) & (df.End_Time_1>=x['Timestamp'])
        #if at least one True return first value of series
        if mask1.any():
                return df.loc[mask1].Sign_1[0]
        #check second start and end time
        else:
                mask2 = (df.S_time2<=x['Timestamp']) & (df.End_time_2>=x['Timestamp'])
                if mask2.any():
                    #if at least one True return first value
                    return df.loc[mask2].Sign_2[0]
                else:
                    #if all False return NaN
                    return np.nan

    out['Bin_time'] = out.apply(search, axis=1)
    #print out.head(10)