パンダのデータフレームの日付列に基づいて辞書のリストを並べ替えます

debugcn 投稿 Dev

デンマーク語

以下に示すような入力リストとデータフレームがあります。

[{"type": "linear",
  "from": "2020-02-04T20:00:00.000Z",
  "to": "2020-02-03T20:00:00.000Z",
  "days":3,
  "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
  },
 {"type": "quadratic",
  "from": "2020-02-03T20:00:00.000Z",
  "to": "2020-02-10T20:00:00.000Z",
  "days":3,
  "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
  },
 {"type": "polynomial",
  "from": "2020-02-05T20:00:00.000Z",
  "to": "2020-02-03T20:00:00.000Z",
  "days":3,
  "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
  }]

df：

Date                t_factor     
2020-02-01             5             
2020-02-02             23              
2020-02-03             14           
2020-02-04             23
2020-02-05             23  
2020-02-06             23          
2020-02-07             30            
2020-02-08             29            
2020-02-09             100
2020-03-10             38
2020-03-11             38               
2020-03-12             38                    
2020-03-13             70           
2020-03-14             70

ステップ1：辞書の「from」キーの値に基づいてリストを並べ替えます

[
 {"type": "quadratic",
      "from": "2020-02-03T20:00:00.000Z",
      "to": "2020-02-10T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },
{"type": "linear",
      "from": "2020-02-04T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },
     {"type": "polynomial",
      "from": "2020-02-05T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      }]

ステップ2：dfの最小日付として「from」キーの値を持つ辞書を追加し、「to」はソートされたリストの最初の辞書の「from」日付である必要があります。"days" = 0、 "coef"：[0.1,0.1,0.1,0.1,0.1,0.1]。

{"type": "df_first",
      "from": "2020-02-01T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      }

ステップ3：「from」キーの値がdfの最小日付の7日後である辞書を追加し、「to」はfromの1日後でなければなりません。

{"type": "df_mid",
      "from": "2020-02-08T20:00:00.000Z",
      "to": "2020-02-09T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      }

ステップ4：dfの最大日付として「from」キーの値を持つ辞書を追加し、「to」は「from」と同じである必要があります。

{"type": "df_last",
      "from": "2020-02-14T20:00:00.000Z",
      "to": "2020-02-14T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      }

手順5：「開始」日付に基づいてすべての辞書を並べ替えます。

期待される出力：

[{"type": "df_first",
      "from": "2020-02-01T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },
     {"type": "quadratic",
      "from": "2020-02-03T20:00:00.000Z",
      "to": "2020-02-10T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },
{"type": "linear",
      "from": "2020-02-04T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },

     {"type": "polynomial",
      "from": "2020-02-05T20:00:00.000Z",
      "to": "2020-02-03T20:00:00.000Z",
      "days":3,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },
{"type": "df_mid",
      "from": "2020-02-08T20:00:00.000Z",
      "to": "2020-02-09T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      },

{"type": "df_last",
      "from": "2020-02-14T20:00:00.000Z",
      "to": "2020-02-14T20:00:00.000Z",
      "days":0,
      "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
      }
]

ステップ6：

各辞書の「to」値を次の辞書の「from」値に置き換えます。最後の辞書の「to」値はそのままです。

期待される最終出力：

[{"type": "df_first",
          "from": "2020-02-01T20:00:00.000Z",
          "to": "2020-02-03T20:00:00.000Z",
          "days":0,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          },
         {"type": "quadratic",
          "from": "2020-02-03T20:00:00.000Z",
          "to": "2020-02-04T20:00:00.000Z",
          "days":3,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          },
    {"type": "linear",
          "from": "2020-02-04T20:00:00.000Z",
          "to": "2020-02-05T20:00:00.000Z",
          "days":3,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          },
    
         {"type": "polynomial",
          "from": "2020-02-05T20:00:00.000Z",
          "to": "2020-02-08T20:00:00.000Z",
          "days":3,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          },
    {"type": "df_mid",
          "from": "2020-02-08T20:00:00.000Z",
          "to": "2020-02-14T20:00:00.000Z",
          "days":0,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          },
    
    {"type": "df_last",
          "from": "2020-02-14T20:00:00.000Z",
          "to": "2020-02-14T20:00:00.000Z",
          "days":0,
          "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
          }
    ]

シュバムシャルマ

機能の定義add_dct辞書のリストとして引数をとりますlstと_type、_fromと_toとに、新しい辞書を追加しますlst：

dmin, dmax = df['Date'].min(), df['Date'].max()
def add_dct(lst, _type, _from, _to):
    lst.append({
        'type': _type,
        'from': _from if isinstance(_from, str) else _from.strftime("%Y-%m-%dT20:%M:%S.000Z"),
        'to': _to if isinstance(_to, str) else _to.strftime("%Y-%m-%dT20:%M:%S.000Z"),
        'days': 0,
        "coef":[0.1,0.1,0.1,0.1,0.1,0.1]
    })

predefined要件に応じて、次の手順に従います。

# STEP 1
lst = sorted(lst, key=lambda d: pd.Timestamp(d['from']))

# STEP 2
add_dct(lst, 'df_first', dmin, lst[0]['from'])

# STEP 3
add_dct(lst, 'df_mid', dmin + pd.Timedelta(days=7), dmin + pd.Timedelta(days=8))

# STEP 4
add_dct(lst, 'df_last', dmax, dmax)

# STEP 5
lst = sorted(lst, key=lambda d: pd.Timestamp(d['from']))

結果：

[{'type': 'df_first',
  'from': '2020-02-01T20:00:00.000Z',
  'to': '2020-02-03T20:00:00.000Z',
  'days': 0,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'quadratic',
  'from': '2020-02-03T20:00:00.000Z',
  'to': '2020-02-10T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'linear',
  'from': '2020-02-04T20:00:00.000Z',
  'to': '2020-02-03T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'polynomial',
  'from': '2020-02-05T20:00:00.000Z',
  'to': '2020-02-03T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'df_mid',
  'from': '2020-02-08T20:00:00.000Z',
  'to': '2020-02-09T20:00:00.000Z',
  'days': 0,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'df_last',
  'from': '2020-03-14T20:00:00.000Z',
  'to': '2020-03-14T20:00:00.000Z',
  'days': 0,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]}]

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