CODEWITHSUNDEEP
pythonpandas
dubbbdan
dubbbdan

Reputation: 2740

Pandas Count zeros in time series

I have a daily time series [1980 - present] where I need to check each daily timestep for zeros and systematically drop records. I would ultimately like to vectorize this solution, so I can pre-process these operations before proceeding with my analysis. If I have the dataframe df:

         date               name  elev_exact      swe
0  1990-10-30   COTTONWOOD_CREEK    2337.816  0.01524
1  1990-10-30    EMIGRANT_SUMMIT    2252.472  0.00000
2  1990-10-30     PHILLIPS_BENCH    2499.360  0.05334
3  1990-10-30    PINE_CREEK_PASS    2048.256  0.00000
4  1990-10-30  SALT_RIVER_SUMMIT    2328.672  0.00000
5  1990-10-30      SEDGWICK_PEAK    2392.680  0.00000
6  1990-10-30          SHEEP_MTN    2026.920  0.00000
7  1990-10-30  SLUG_CREEK_DIVIDE    2202.180  0.00000
8  1990-10-30       SOMSEN_RANCH    2072.640  0.00000
9  1990-10-30   WILDHORSE_DIVIDE    1978.152  0.00000
10 1990-10-30       WILLOW_CREEK    2462.784  0.01778
11 1991-03-15   COTTONWOOD_CREEK    2337.816  0.41910
12 1991-03-15    EMIGRANT_SUMMIT    2252.472  0.42418
13 1991-03-15     PHILLIPS_BENCH    2499.360  0.52832
14 1991-03-15    PINE_CREEK_PASS    2048.256  0.32258
15 1991-03-15  SALT_RIVER_SUMMIT    2328.672  0.23876
16 1991-03-15      SEDGWICK_PEAK    2392.680  0.39878
17 1991-03-15          SHEEP_MTN    2026.920  0.31242
18 1991-03-15  SLUG_CREEK_DIVIDE    2202.180  0.29464
19 1991-03-15       SOMSEN_RANCH    2072.640  0.29972
20 1991-03-15   WILDHORSE_DIVIDE    1978.152  0.35052
21 1991-03-15       WILLOW_CREEK    2462.784  0.60706
22 1991-10-25   COTTONWOOD_CREEK    2337.816  0.01270
23 1991-10-25    EMIGRANT_SUMMIT    2252.472  0.01016
24 1991-10-25     PHILLIPS_BENCH    2499.360  0.02286
25 1991-10-25    PINE_CREEK_PASS    2048.256  0.00508
26 1991-10-25  SALT_RIVER_SUMMIT    2328.672  0.01016
27 1991-10-25      SEDGWICK_PEAK    2392.680  0.00254
28 1991-10-25          SHEEP_MTN    2026.920  0.00000
29 1991-10-25  SLUG_CREEK_DIVIDE    2202.180  0.00762
30 1991-10-25       SOMSEN_RANCH    2072.640  0.00000
31 1991-10-25   WILDHORSE_DIVIDE    1978.152  0.00508
32 1991-10-25       WILLOW_CREEK    2462.784  0.02032

The problem is I want to find days where more than one zero swe measurement, and only keep the observation with the largest elev_exact. I then need to merge the desired zero record back into df.

Here is a groupby loop that would achieve what I want:

result = pd.DataFrame()
for name, group in df.groupby('date'):

    non_zero = group.where(group.swe >0).dropna()

    if not group.equals(non_zero):
        zeros = group.where(group.swe == 0).dropna() 
        zero_kept = zeros.loc[zeros.elev_exact.idxmax()]
        out = non_zero.append(zero_kept)
        out = out[out.elev_exact >= zero_kept.elev_exact]
        result = pd.concat([result, out])
    else:
        result = pd.concat([result, non_zero])

I dont mind using groupby but I would like to use it a little more methodically so I don't have the inner if-else loop.

Here is how I am thinking about the problem

  1. For each daily timestep, I want to find where there are more than one zero measurement
zero_count = df.groupby('date').apply(lambda x: np.count_nonzero(x==0))
zero_count = zero_count.where(zero_count >1).dropna()
  1. Separate dates with where zero_count > 1
zero_fix = zero_count.where(zero_count >1).dropna()
  1. Find the maximum elevation for each day with multiple zeros
fixes = df[df.date.isin(zero_fix.index)].dropna()
fixes = fixes.loc[fixes[fixes.swe==0].groupby('date')['elev_exact'].idxmax().to_list()]
  1. Apply the found elevation thresholds back to df. 
df.loc[:,'threshold'] = df.date.map(lu_dict)
df = df.replace(np.nan, 0)
df = df[df.elev_exact >= df.threshold].drop('threshold', axis=1)

This also works, but the lambda function is step 1 is pretty slow. Is there another way to count zeros?

Expected output:

          date               name  elev_exact      swe
2   1990-10-30     PHILLIPS_BENCH    2499.360  0.05334
5   1990-10-30      SEDGWICK_PEAK    2392.680  0.00000
10  1990-10-30       WILLOW_CREEK    2462.784  0.01778
11  1991-03-15   COTTONWOOD_CREEK    2337.816  0.41910
12  1991-03-15    EMIGRANT_SUMMIT    2252.472  0.42418
13  1991-03-15     PHILLIPS_BENCH    2499.360  0.52832
14  1991-03-15    PINE_CREEK_PASS    2048.256  0.32258
15  1991-03-15  SALT_RIVER_SUMMIT    2328.672  0.23876
16  1991-03-15      SEDGWICK_PEAK    2392.680  0.39878
17  1991-03-15          SHEEP_MTN    2026.920  0.31242
18  1991-03-15  SLUG_CREEK_DIVIDE    2202.180  0.29464
19  1991-03-15       SOMSEN_RANCH    2072.640  0.29972
20  1991-03-15   WILDHORSE_DIVIDE    1978.152  0.35052
21  1991-03-15       WILLOW_CREEK    2462.784  0.60706
22  1991-10-25   COTTONWOOD_CREEK    2337.816  0.01270
23  1991-10-25    EMIGRANT_SUMMIT    2252.472  0.01016
24  1991-10-25     PHILLIPS_BENCH    2499.360  0.02286
26  1991-10-25  SALT_RIVER_SUMMIT    2328.672  0.01016
27  1991-10-25      SEDGWICK_PEAK    2392.680  0.00254
29  1991-10-25  SLUG_CREEK_DIVIDE    2202.180  0.00762
30  1991-10-25       SOMSEN_RANCH    2072.640  0.00000
32  1991-10-25       WILLOW_CREEK    2462.784  0.02032

Upvotes: 3

Views: 440

Answers (1)

Scott Boston
Scott Boston

Reputation: 153460

You can try this, split the dataframe into non-zeroes and zeroes, then sort zeroes dataframe by highest elev_exact and use drop_duplicates with subset on date column. Lastly, use pd.concat to join dataframe back together and sort:

df_nonzeroes = df[df['swe'].ne(0)]
df_zeroes = df[df['swe'].eq(0)].sort_values('elev_exact', ascending=False).drop_duplicates(subset=['date'])

df_out = pd.concat([df_nonzeroes, df_zeroes]).sort_index()
print(df_out)

Output:

          date               name  elev_exact      swe
0   1990-10-30   COTTONWOOD_CREEK    2337.816  0.01524
2   1990-10-30     PHILLIPS_BENCH    2499.360  0.05334
5   1990-10-30      SEDGWICK_PEAK    2392.680  0.00000
10  1990-10-30       WILLOW_CREEK    2462.784  0.01778
11  1991-03-15   COTTONWOOD_CREEK    2337.816  0.41910
12  1991-03-15    EMIGRANT_SUMMIT    2252.472  0.42418
13  1991-03-15     PHILLIPS_BENCH    2499.360  0.52832
14  1991-03-15    PINE_CREEK_PASS    2048.256  0.32258
15  1991-03-15  SALT_RIVER_SUMMIT    2328.672  0.23876
16  1991-03-15      SEDGWICK_PEAK    2392.680  0.39878
17  1991-03-15          SHEEP_MTN    2026.920  0.31242
18  1991-03-15  SLUG_CREEK_DIVIDE    2202.180  0.29464
19  1991-03-15       SOMSEN_RANCH    2072.640  0.29972
20  1991-03-15   WILDHORSE_DIVIDE    1978.152  0.35052
21  1991-03-15       WILLOW_CREEK    2462.784  0.60706
22  1991-10-25   COTTONWOOD_CREEK    2337.816  0.01270
23  1991-10-25    EMIGRANT_SUMMIT    2252.472  0.01016
24  1991-10-25     PHILLIPS_BENCH    2499.360  0.02286
25  1991-10-25    PINE_CREEK_PASS    2048.256  0.00508
26  1991-10-25  SALT_RIVER_SUMMIT    2328.672  0.01016
27  1991-10-25      SEDGWICK_PEAK    2392.680  0.00254
29  1991-10-25  SLUG_CREEK_DIVIDE    2202.180  0.00762
30  1991-10-25       SOMSEN_RANCH    2072.640  0.00000
31  1991-10-25   WILDHORSE_DIVIDE    1978.152  0.00508
32  1991-10-25       WILLOW_CREEK    2462.784  0.02032

Upvotes: 2

Related Questions