danielhagnoul

Cette question a été posée sur le forum Python: https://www.developpez.net/forums/d2.../#post11649856

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using DataFrames
 
df = DataFrame()
 
df.A = [
    "2019-02-01",
    "2019-02-02",
    "2019-02-03",
    "2019-02-04",
    "2019-02-05",
    "2019-02-06",
    "2019-02-07",
    "2019-02-08",
    "2019-02-09",
    "2019-02-10",
    "2019-02-11",
    "2019-02-12",
    "2019-02-13",
    "2019-02-14",
    "2019-02-15",
    "2019-02-16",
    "2019-02-17",
    "2019-02-18",
    "2019-02-19",
    "2019-02-20",
    "2019-02-21",
    "2019-02-22",
    "2019-02-23",
    "2019-02-24",
    "2019-02-25",
    "2019-02-26",
    "2019-02-27",
    "2019-02-28",
]
 
df.B = [
    236.182,
    2169.59,
    831.993,
    358.897,
    318.064,
    284.661,
    501.12,
    367.595,
    306.555,
    838.952,
    390.737,
    326.779,
    243.793,
    183.42,
    241.558,
    221.979,
    169.057,
    228.134,
    180.55,
    138.609,
    142.635,
    122.321,
    124.902,
    112.064,
    101.883,
    122.155,
    122.149,
    101.846,
]
 
max = length(df[:, :B])
 
df.C = [0.0 for _=1:max]
 
index_min = [(i + argmin(df.B[i:(i+5<=max ? i+5 : max)]) - 1) for i=1:5:max]
 
for i in index_min
    df.C[i] = df.B[i]
end
 
println(df)
println(df) # nécessaire pour afficher toutes les lignes du df

#=
28×3 DataFrame
 Row │ A           B         C       
     │ String      Float64   Float64 
─────┼───────────────────────────────
   1 │ 2019-02-01   236.182  236.182 
   2 │ 2019-02-02  2169.59     0.0   
   3 │ 2019-02-03   831.993    0.0   
   4 │ 2019-02-04   358.897    0.0   
   5 │ 2019-02-05   318.064    0.0   
   6 │ 2019-02-06   284.661  284.661 
   7 │ 2019-02-07   501.12     0.0   
   8 │ 2019-02-08   367.595    0.0   
   9 │ 2019-02-09   306.555    0.0   
  10 │ 2019-02-10   838.952    0.0   
  11 │ 2019-02-11   390.737    0.0   
  12 │ 2019-02-12   326.779    0.0   
  13 │ 2019-02-13   243.793    0.0   
  14 │ 2019-02-14   183.42   183.42  
  15 │ 2019-02-15   241.558    0.0   
  16 │ 2019-02-16   221.979    0.0   
  17 │ 2019-02-17   169.057    0.0   
  18 │ 2019-02-18   228.134    0.0   
  19 │ 2019-02-19   180.55     0.0   
  20 │ 2019-02-20   138.609  138.609 
  21 │ 2019-02-21   142.635    0.0   
  22 │ 2019-02-22   122.321    0.0   
  23 │ 2019-02-23   124.902    0.0   
  24 │ 2019-02-24   112.064    0.0   
  25 │ 2019-02-25   101.883  101.883 
  26 │ 2019-02-26   122.155    0.0   
  27 │ 2019-02-27   122.149    0.0   
  28 │ 2019-02-28   101.846  101.846 
=#

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