{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## This competition predicts how much fans will engage in digital content like \"reactions\" and \"actions\" the next day for each NLB player id\n\n## このコンペは、プレイヤーID毎に各種数値から4つの目的変数を予測するコンペになります。\n\n## 複数のcsvファイルが存在し、それを結合していく必要があります。\n\n## 本カーネルはhttps://www.kaggle.com/chumajin/eda-of-mlb-for-starter-versionを参考にしております。\n\n## 違いとしては、細かな入出力の確認や、一部文法の解説を交えています。\n\n## また、本カーネルの範囲としてはデータ整形（前処理）までとします。モデルを用いた特徴量エンジニアリングは特段しておりません。\n\n","metadata":{}},{"cell_type":"code","source":"import gc\nimport sys\nimport warnings\nfrom pathlib import Path\n\nimport os\n\nimport ipywidgets as widgets\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nwarnings.simplefilter(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:16.188911Z","iopub.execute_input":"2021-06-15T07:28:16.189535Z","iopub.status.idle":"2021-06-15T07:28:17.044465Z","shell.execute_reply.started":"2021-06-15T07:28:16.189391Z","shell.execute_reply":"2021-06-15T07:28:17.043731Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 何を予測するか（submissionファイルを確認する）","metadata":{}},{"cell_type":"code","source":"# 目的変数はtarget1〜4を予測します。\nexample_sample_submission = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/example_sample_submission.csv\")\nexample_sample_submission","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.045570Z","iopub.execute_input":"2021-06-15T07:28:17.045952Z","iopub.status.idle":"2021-06-15T07:28:17.091534Z","shell.execute_reply.started":"2021-06-15T07:28:17.045924Z","shell.execute_reply":"2021-06-15T07:28:17.090631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 説明変数の確認（testデータより）\n\n下記を見ると、json形式がdata-frameに入っていることがわかる。\n\nこれらを分解して、整形し、結合していく必要がある。","metadata":{}},{"cell_type":"code","source":"# rosters：チームの公式戦に出場できる資格を持つ選手登録枠のこと\nexample_test = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/example_test.csv\")\nexample_test","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.093015Z","iopub.execute_input":"2021-06-15T07:28:17.093270Z","iopub.status.idle":"2021-06-15T07:28:17.820319Z","shell.execute_reply.started":"2021-06-15T07:28:17.093247Z","shell.execute_reply":"2021-06-15T07:28:17.819062Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Helper function to unpack json found in daily \n# json_strがna（空白）ならば、nanを返す。\n# それ以外なら、pandasでjson文字列・ファイルを読み込む\ndef unpack_json(json_str):\n    return np.nan if pd.isna(json_str) else pd.read_json(json_str)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.822232Z","iopub.execute_input":"2021-06-15T07:28:17.822633Z","iopub.status.idle":"2021-06-15T07:28:17.828544Z","shell.execute_reply.started":"2021-06-15T07:28:17.822589Z","shell.execute_reply":"2021-06-15T07:28:17.827634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"example_test.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.829700Z","iopub.execute_input":"2021-06-15T07:28:17.829975Z","iopub.status.idle":"2021-06-15T07:28:17.943658Z","shell.execute_reply.started":"2021-06-15T07:28:17.829949Z","shell.execute_reply":"2021-06-15T07:28:17.942836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"example_test[\"games\"].iloc[0] の中身を見てみる","metadata":{}},{"cell_type":"code","source":"example_test[\"games\"].iloc[0]","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.944829Z","iopub.execute_input":"2021-06-15T07:28:17.945075Z","iopub.status.idle":"2021-06-15T07:28:17.953327Z","shell.execute_reply.started":"2021-06-15T07:28:17.945051Z","shell.execute_reply":"2021-06-15T07:28:17.952409Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# example_testの「games」列の1行目。つまり、20210426のデータを適用する。\n# nullの項目はpandasで読み込んだ際に「NaN」に変更されている\nunpack_json(example_test[\"games\"].iloc[0])","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:17.954889Z","iopub.execute_input":"2021-06-15T07:28:17.955565Z","iopub.status.idle":"2021-06-15T07:28:18.023580Z","shell.execute_reply.started":"2021-06-15T07:28:17.955524Z","shell.execute_reply":"2021-06-15T07:28:18.022685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"example_test[\"rosters\"].iloc[0] の中身を見てみる","metadata":{}},{"cell_type":"code","source":"unpack_json(example_test[\"rosters\"].iloc[0])","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:18.025769Z","iopub.execute_input":"2021-06-15T07:28:18.026034Z","iopub.status.idle":"2021-06-15T07:28:18.051010Z","shell.execute_reply.started":"2021-06-15T07:28:18.026007Z","shell.execute_reply":"2021-06-15T07:28:18.049985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"---------以上を踏まえて、trainデータなど他のデータを見ていきます---------","metadata":{}},{"cell_type":"markdown","source":"# trainデータを確認する","metadata":{}},{"cell_type":"code","source":"# test.csvとの違いは2列目の「nextDayPlayerEngagement」があるところ⇨目的変数（予測対象）\ntraining = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/train.csv\")\ntraining","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:28:18.053293Z","iopub.execute_input":"2021-06-15T07:28:18.053688Z","iopub.status.idle":"2021-06-15T07:29:20.075902Z","shell.execute_reply.started":"2021-06-15T07:28:18.053642Z","shell.execute_reply":"2021-06-15T07:29:20.074938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# nextDayPlayerEngagementの中身を見てみる！\n# target1〜4の情報が入っている（予測対象）\nunpack_json(training[\"nextDayPlayerEngagement\"].iloc[0])","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.077279Z","iopub.execute_input":"2021-06-15T07:29:20.077894Z","iopub.status.idle":"2021-06-15T07:29:20.109411Z","shell.execute_reply.started":"2021-06-15T07:29:20.077852Z","shell.execute_reply":"2021-06-15T07:29:20.108416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dateはdatetimeに変換\ntraining['date'] = pd.to_datetime(training['date'], format=\"%Y%m%d\")\ntraining","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.110823Z","iopub.execute_input":"2021-06-15T07:29:20.111216Z","iopub.status.idle":"2021-06-15T07:29:20.299167Z","shell.execute_reply.started":"2021-06-15T07:29:20.111173Z","shell.execute_reply":"2021-06-15T07:29:20.298469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.300068Z","iopub.execute_input":"2021-06-15T07:29:20.300426Z","iopub.status.idle":"2021-06-15T07:29:20.320635Z","shell.execute_reply.started":"2021-06-15T07:29:20.300398Z","shell.execute_reply":"2021-06-15T07:29:20.319549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1216日分のデータ。nullデータは無し。nanデータがところどころにある。\n※ NULLはデータが存在しないことを示すデータ型です。nanは本来データが存在しているが何らかの理由で存在しないことを示す","metadata":{}},{"cell_type":"markdown","source":"---------------------------------------------------------------------","metadata":{}},{"cell_type":"markdown","source":"## ここから**カラムごとにデータがあるところのjsonを事例として1つ見てみます**。","metadata":{}},{"cell_type":"code","source":"training.columns","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.322026Z","iopub.execute_input":"2021-06-15T07:29:20.322402Z","iopub.status.idle":"2021-06-15T07:29:20.328190Z","shell.execute_reply.started":"2021-06-15T07:29:20.322361Z","shell.execute_reply":"2021-06-15T07:29:20.327348Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1つ1つ入力するのが、めんどくさいので、naを抜いて、n番目(0だと一番上)のサンプルをdataframeにしてcolumn名と中身を見る関数を作っちゃいます。","metadata":{}},{"cell_type":"code","source":"# dropnaメソッド：すべての値が欠損値である行・列を削除する: how='all'がデフォルト\ndef exshow(col,n):\n    tmp = training[col]\n    tmp = tmp.dropna()\n    tmpdf = unpack_json(tmp.iloc[n])\n    print(tmpdf.columns)\n    return tmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.329385Z","iopub.execute_input":"2021-06-15T07:29:20.329638Z","iopub.status.idle":"2021-06-15T07:29:20.340146Z","shell.execute_reply.started":"2021-06-15T07:29:20.329615Z","shell.execute_reply":"2021-06-15T07:29:20.339495Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## train_csvの列「nextDayPlayerEngagement」　Jsonを見てみる。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.341173Z","iopub.execute_input":"2021-06-15T07:29:20.341590Z","iopub.status.idle":"2021-06-15T07:29:20.372070Z","shell.execute_reply.started":"2021-06-15T07:29:20.341560Z","shell.execute_reply":"2021-06-15T07:29:20.371310Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#　行数が先ほどと変わらないため、欠損値行の削除が行われなかった！\ntmpdf = exshow(\"nextDayPlayerEngagement\",0)\ntmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.373272Z","iopub.execute_input":"2021-06-15T07:29:20.373813Z","iopub.status.idle":"2021-06-15T07:29:20.410851Z","shell.execute_reply.started":"2021-06-15T07:29:20.373770Z","shell.execute_reply":"2021-06-15T07:29:20.409670Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# tmpdfの欠損値の値がどれくらいあるか確認してみる\n# 欠損値は存在しないことが下記よりわかる\ntmpdf.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.412332Z","iopub.execute_input":"2021-06-15T07:29:20.412801Z","iopub.status.idle":"2021-06-15T07:29:20.434081Z","shell.execute_reply.started":"2021-06-15T07:29:20.412758Z","shell.execute_reply":"2021-06-15T07:29:20.432867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* engagementMetricsDate - 米国太平洋時間に基づくプレーヤーエンゲージメント指標の日付（前日のゲーム、名簿、フィールド統計、トランザクション、賞などと一致します）。\n* playerId\n* target1\n* target2\n* target3\n* target4\n\n\ntarget1-target4は、0から100のスケールでのデジタルエンゲージメントの毎日のインデックスです。","metadata":{}},{"cell_type":"markdown","source":"## games(train.csvのcolumn2番目)\n特定の日のすべてのゲーム情報を含むネストされた JSON。レギュラー シーズン、ポストシーズン、オールスター ゲームに加えて、スプリング トレーニングとエキシビション ゲームが含まれています。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.435493Z","iopub.execute_input":"2021-06-15T07:29:20.435897Z","iopub.status.idle":"2021-06-15T07:29:20.463388Z","shell.execute_reply.started":"2021-06-15T07:29:20.435855Z","shell.execute_reply":"2021-06-15T07:29:20.462596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"games\",1) # 0番目（1番上はデータが一行しかなかったので、1にしました。)\ntmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.464688Z","iopub.execute_input":"2021-06-15T07:29:20.465077Z","iopub.status.idle":"2021-06-15T07:29:20.506807Z","shell.execute_reply.started":"2021-06-15T07:29:20.465036Z","shell.execute_reply":"2021-06-15T07:29:20.505956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"カラムの意味の翻訳は↓を開いてください。(長いので、hideしています。)","metadata":{}},{"cell_type":"markdown","source":"* gamePk  : ゲームの一意の識別子。\n* gameType  :   ゲームの種類、さまざまな種類がここにあります。\n* season : \n* gameDate : \n* gameTimeUTC  : UTCでの始球式。\n* resumeDate  :  タイムゲームが再開されました（放棄された場合、それ以外の場合はnull）。\n* resumedFrom  :  タイムゲームは元々放棄されていました（放棄された場合、それ以外の場合はnull）。\n* codedGameState  :  ゲームのステータスコード、さまざまなタイプがここにあります。\n* detailedGameState  :  ゲームのステータス、さまざまな種類がここにあります。\n* isTie  :  ブール値。ゲームが引き分けで終了した場合はtrue。\n* gameNumber  :  ダブルヘッダーを区別するためのゲーム番号フラグ\n* doubleHeader  :  YはDH、Nはシングルゲーム、Sはスプリット\n* dayNight  :  スケジュールされた開始時間の昼または夜のフラグ。\n* scheduledInnings  :  予定イニング数。\n* gamesInSeries  :  現在のシリーズのゲーム数。\n* seriesDescription  :  現在のシリーズのテキスト説明。\n* homeId  :  ホームチームの一意の識別子。\n* homeName  :  ホームチーム名。\n* homeAbbrev  :  ホームチームの略語。\n* homeWins  :  ホームチームのシーズンの現在の勝利数。\n* homeLosses  :  ホームチームのシーズンでの現在の損失数。\n* homeWinPct  :  ホームチームの現在の勝率。\n* homeWinner  :  ブール値。ホームチームが勝った場合はtrue。\n* homeScore  :  ホームチームが得点するラン。\n* awayId  :  アウェイチームの一意の識別子。\n* awayName  :  アウェイチームの一意の識別子。\n* awayAbbrev  :  アウェイチームの略。\n* awayWins  :  アウェイチームのシーズンの現在の勝利数。\n* awayLosses  :  アウェイチームのシーズン中の現在の敗北数。\n* awayWinPct  :  アウェイチームの現在の勝率。\n* awayWinner  :  ブール値。離れたチームが勝った場合はtrue。\n* awayScore  :  アウェイチームが得点したラン。","metadata":{"_kg_hide-input":true}},{"cell_type":"markdown","source":"## rosters(train.csvのcolumn3番目)\n特定の日のすべての名簿情報を含むネストされた JSON。インシーズンとオフシーズンのチーム名簿が含まれます。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.508094Z","iopub.execute_input":"2021-06-15T07:29:20.508637Z","iopub.status.idle":"2021-06-15T07:29:20.531015Z","shell.execute_reply.started":"2021-06-15T07:29:20.508562Z","shell.execute_reply":"2021-06-15T07:29:20.530086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"rosters\",0) \ntmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.533524Z","iopub.execute_input":"2021-06-15T07:29:20.533798Z","iopub.status.idle":"2021-06-15T07:29:20.562552Z","shell.execute_reply.started":"2021-06-15T07:29:20.533771Z","shell.execute_reply":"2021-06-15T07:29:20.561521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* playerId-プレーヤーの一意の識別子。\n* gameDate\n* teamId-そのプレーヤーがその日にいるteamId。\n* statusCode-名簿ステータスの略語。\n* status-説明的な名簿のステータス。","metadata":{}},{"cell_type":"markdown","source":"## playerBoxScores(train.csvのcolumn4番目)\n特定の日のプレイヤー ゲーム レベルで集計されたゲーム統計を含むネストされた JSON。レギュラーシーズン、ポストシーズン、オールスターゲームが含まれます。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.563732Z","iopub.execute_input":"2021-06-15T07:29:20.564092Z","iopub.status.idle":"2021-06-15T07:29:20.587093Z","shell.execute_reply.started":"2021-06-15T07:29:20.564052Z","shell.execute_reply":"2021-06-15T07:29:20.585935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"playerBoxScores\",0) \ntmpdf.head(5)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.591372Z","iopub.execute_input":"2021-06-15T07:29:20.591626Z","iopub.status.idle":"2021-06-15T07:29:20.654316Z","shell.execute_reply.started":"2021-06-15T07:29:20.591599Z","shell.execute_reply":"2021-06-15T07:29:20.653532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* home  : バイナリ、ホームチームの場合は1、離れている場合は0。\n* gamePk  :   ゲームの一意の識別子。\n* gameDate : \n* gameTimeUTC  : UTCでの始球式。\n* teamId  :   チームの一意の識別子。\n* teamName : \n* playerId  : プレーヤーの一意の識別子。\n* playerName : \n* jerseyNum : \n* positionCode  : 番号の位置コード、詳細はこちらです。\n* positionName  :  テキスト位置の表示、詳細はこちらです。\n* positionType  :  ポジショングループ、詳細はこちらです。\n* battingOrder  :  形式：「###」。最初の桁は打順スポットを示し、次の2桁はそのプレーヤーがその打順スポットを占めた順序を示します。例：「300」は、打順の3番目のスポットのスターターを示します。 4人目（900、901、902以降）が打順9位を占めることを示す「903」。ゲームに登場した場合にのみ入力されます。\n* gamesPlayedBatting  :  プレーヤーが打者、ランナー、または野手としてゲームに参加した場合は1。\n* flyOuts  :  ゲームの合計フライアウト。\n* groundOuts  :  ゲームのトータルグラウンドアウト。\n* runsScored  :  ゲームの合計ランが記録されました。\n* doubles  :  ゲームの合計は2倍です。\n* triples  :  ゲームの合計トリプル。\n* homeRuns  :  ゲームの総本塁打。\n* strikeOuts  :  ゲームの合計三振。\n* baseOnBalls  :  ゲームの合計ウォーク。\n* intentionalWalks  :  ゲームの故意四球。\n* hits  :  ゲームの総ヒット数。\n* hitByPitch  :  ピッチによるゲームの合計ヒット。\n* atBats  :  でのゲーム合計\n* caughtStealing  :  ゲームの合計が盗塁をキャッチしました。\n* stolenBases  :  ゲームの盗塁総数。\n* groundIntoDoublePlay  :  ゲームの合計併殺はに基づいています。\n* groundIntoTriplePlay  :  ゲームの合計 3 回プレイが基礎になります。\n* plateAppearances  :  ゲームの総打席。\n* totalBases  :  ゲームの総拠点数。\n* rbi  :  ゲームの合計打点。\n* leftOnBase  :  ゲームの総ランナーはベースに残った。\n* sacBunts  :  ゲームの合計犠牲バント。\n* sacFlies  :  ゲームの総犠牲フライ。\n* catchersInterference  :  ゲームのトータルキャッチャーの干渉が発生しました。\n* pickoffs  :  ゲームの合計回数がベースから外れました。\n* gamesPlayedPitching :  バイナリ、プレーヤーが投手としてゲームに参加した場合は 1。\n* gamesStartedPitching :  バイナリ、プレーヤーがゲームの先発投手だった場合は1。\n* completeGamesPitching  :  バイナリ、完投でクレジットされている場合は1。\n* shutoutsPitching  :  バイナリ、完封でクレジットされている場合は1。\n* winsPitching  :  バイナリ、勝利でクレジットされている場合は 1。\n* lossesPitching  :  バイナリ、損失がクレジットされている場合は1。\n* flyOutsPitching  :  許可されたフライアウトのゲーム合計。\n* airOutsPitching  :  エアアウト（フライアウト+ポップアウト）のゲーム合計が許可されます。\n* groundOutsPitching  :  ゲームの合計グラウンドアウトが許可されます。\n* runsPitching  :  ゲームの合計実行が許可されます。\n* doublesPitching  :  ゲームの合計は2倍になります。\n* triplesPitching  :  ゲームの合計トリプルが許可されます。\n* homeRunsPitching  :  ゲームの合計ホームランが許可されます。\n* strikeOutsPitching  :  ゲームの合計三振が許可されます。\n* baseOnBallsPitching  :  ゲームの合計歩行が許可されます。\n* intentionalWalksPitching  :  ゲームの故意四球の合計が許可されます。\n* hitsPitching  :  許可されるゲームの合計ヒット数。\n* hitByPitchPitching  :  許可されたピッチによるゲームの合計ヒット。\n* atBatsPitching  :  でのゲーム合計\n* caughtStealingPitching  :  ゲームの合計は、盗みをキャッチしました。\n* stolenBasesPitching  :  ゲームの盗塁の合計は許可されます。\n* inningsPitched  :  ゲームの総投球回。\n* saveOpportunities  :  バイナリ、保存の機会がある場合は1。\n* earnedRuns  :  ゲームの合計自責点は許可されています。\n* battersFaced  :  直面したゲームの総打者。\n* outsPitching  :  ゲームの合計アウトが記録されました。\n* pitchesThrown  :  投げられた投球のゲーム総数。\n* balls  :  投げられたゲームの合計ボール。\n* strikes  :  スローされたゲームの合計ストライク。\n* hitBatsmen  :  ゲームの総死球打者。\n* balks  :  ゲームの合計はボークします。\n* wildPitches  :  投げられた暴投のゲーム総数。\n* pickoffsPitching  :  ゲームのピックオフの総数。\n* rbiPitching  :  打点のゲーム総数は許可されています。\n* inheritedRunners  :  継承されたランナーのゲーム合計を想定。\n* inheritedRunnersScored :  得点した継承されたランナーのゲーム合計。\n* catchersInterferencePitching  :  キャッチャーの干渉のゲーム合計はバッテリーによって発生しました。\n* sacBuntsPitching  :  ゲームの犠牲バントの合計が許可されます。\n* sacFliesPitching  :  ゲームの犠牲フライは許可されています。\n* saves  :  バイナリ、保存でクレジットされている場合は1。\n* holds  :  バイナリ、保留がクレジットされている場合は1。\n* blownSaves  :  バイナリ、ブローセーブでクレジットされている場合は1。\n* assists  :  ゲームのアシスト総数。\n* putOuts  :  ゲームの刺殺の総数。\n* errors  :  ゲームのエラーの総数。\n* chances  :  ゲームのトータルフィールディングチャンス。","metadata":{"_kg_hide-input":true}},{"cell_type":"markdown","source":"## teamBoxScores(train.csvのcolumn5番目)\n特定の日のチーム ゲーム レベルで集計されたゲーム統計を含むネストされた JSON。レギュラーシーズン、ポストシーズン、オールスターゲームが含まれます。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.656457Z","iopub.execute_input":"2021-06-15T07:29:20.656732Z","iopub.status.idle":"2021-06-15T07:29:20.678514Z","shell.execute_reply.started":"2021-06-15T07:29:20.656694Z","shell.execute_reply":"2021-06-15T07:29:20.677671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"teamBoxScores\",0) \ntmpdf.head(5)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.679927Z","iopub.execute_input":"2021-06-15T07:29:20.680208Z","iopub.status.idle":"2021-06-15T07:29:20.726471Z","shell.execute_reply.started":"2021-06-15T07:29:20.680159Z","shell.execute_reply":"2021-06-15T07:29:20.725624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* home  : バイナリ、ホームチームの場合は1、離れている場合は0。\n* teamId  :   チームの一意の識別子。\n* gamePk  :   ゲームの一意の識別子。\n* gameDate : \n* gameTimeUTC  : UTCでの始球式。\n* flyOuts  :  ゲームの合計フライアウト。\n* groundOuts  :  ゲームのトータルグラウンドアウト。\n* runsScored  :  ゲームの合計ランが記録されました。\n* doubles  :  ゲームの合計は2倍です。\n* triples  :  ゲームの合計トリプル。\n* homeRuns  :  ゲームの総本塁打。\n* strikeOuts  :  ゲームの合計三振。\n* baseOnBalls  :  ゲームの合計ウォーク。\n* intentionalWalks  :  ゲームの故意四球。\n* hits  :  ゲームの総ヒット数。\n* hitByPitch  :  ピッチによるゲームの合計ヒット。\n* atBats  :  でのゲーム合計\n* caughtStealing  :  ゲームの合計が盗塁をキャッチしました。\n* stolenBases  :  ゲームの盗塁総数。\n* groundIntoDoublePlay  :  ゲームの合計併殺はに基づいています。\n* groundIntoTriplePlay  :  ゲームの合計 3 回プレイが基礎になります。\n* plateAppearances  :  ゲームの総打席。\n* totalBases  :  ゲームの総拠点数。\n* rbi  :  ゲームの合計打点。\n* leftOnBase  :  ゲームの総ランナーはベースに残った。\n* sacBunts  :  ゲームの合計犠牲バント。\n* sacFlies  :  ゲームの総犠牲フライ。\n* catchersInterference  :  ゲームのトータルキャッチャーの干渉が発生しました。\n* pickoffs  :  ゲームの合計回数がベースから外れました。\n* airOutsPitching  :  エアアウト（フライアウト+ポップアウト）のゲーム合計が許可されます。\n* groundOutsPitching  :  ゲームの合計グラウンドアウトが許可されます。\n* runsPitching  :  ゲームの合計実行が許可されます。\n* doublesPitching  :  ゲームの合計は2倍になります。\n* triplesPitching  :  ゲームの合計トリプルが許可されます。\n* homeRunsPitching  :  ゲームの合計ホームランが許可されます。\n* strikeOutsPitching  :  ゲームの合計三振が許可されます。\n* baseOnBallsPitching  :  ゲームの合計歩行が許可されます。\n* intentionalWalksPitching  :  ゲームの故意四球の合計が許可されます。\n* hitsPitching  :  許可されるゲームの合計ヒット数。\n* hitByPitchPitching  :  許可されたピッチによるゲームの合計ヒット。\n* atBatsPitching  :  でのゲーム合計\n* caughtStealingPitching  :  ゲームの合計は、盗みをキャッチしました。\n* stolenBasesPitching  :  ゲームの盗塁の合計は許可されます。\n* inningsPitched  :  ゲームの総投球回。\n* earnedRuns  :  ゲームの合計自責点は許可されています。\n* battersFaced  :  直面したゲームの総打者。\n* outsPitching  :  ゲームの合計アウトが記録されました。\n* hitBatsmen  :  ゲームの総死球打者。\n* balks  :  ゲームの合計はボークします。\n* wildPitches  :  投げられた暴投のゲーム総数。\n* pickoffsPitching  :  ゲームのピックオフの総数。\n* rbiPitching  :  打点のゲーム総数は許可されています。\n* inheritedRunners  :  継承されたランナーのゲーム合計を想定。\n* inheritedRunnersScored :  得点した継承されたランナーのゲーム合計。\n* catchersInterferencePitching  :  キャッチャーの干渉のゲーム合計はバッテリーによって発生しました。\n* sacBuntsPitching  :  ゲームの犠牲バントの合計が許可されます。\n* sacFliesPitching  :  ゲームの犠牲フライは許可されています。","metadata":{"_kg_hide-input":true}},{"cell_type":"markdown","source":"## transactions(train.csvのcolumn6番目)\n特定の日の MLB チームに関係するすべてのトランザクション情報を含むネストされた JSON。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.727664Z","iopub.execute_input":"2021-06-15T07:29:20.727937Z","iopub.status.idle":"2021-06-15T07:29:20.751050Z","shell.execute_reply.started":"2021-06-15T07:29:20.727909Z","shell.execute_reply":"2021-06-15T07:29:20.750098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"transactions\",1) \ntmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.752027Z","iopub.execute_input":"2021-06-15T07:29:20.752293Z","iopub.status.idle":"2021-06-15T07:29:20.786883Z","shell.execute_reply.started":"2021-06-15T07:29:20.752266Z","shell.execute_reply":"2021-06-15T07:29:20.785946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* transactionId  : トランザクションの一意の識別子。\n* playerId  :   プレーヤーの一意の識別子。\n* playerName : \n* date : \n* fromTeamId  : プレーヤーの出身チームの一意の識別子。\n* fromTeamName : \n* toTeamId  :   プレーヤーが行くチームの一意の識別子。\n* toTeamName : \n* effectiveDate : \n* resolutionDate : \n* typeCode  : トランザクションステータスの略語。\n* typeDesc  :   トランザクションステータスの説明。\n* description  :   トランザクションのテキスト説明。","metadata":{}},{"cell_type":"markdown","source":"## standings(train.csvのcolumn7番目)\n特定の日の MLB チームに関するすべての順位情報を含むネストされた JSON。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.788160Z","iopub.execute_input":"2021-06-15T07:29:20.788529Z","iopub.status.idle":"2021-06-15T07:29:20.811991Z","shell.execute_reply.started":"2021-06-15T07:29:20.788488Z","shell.execute_reply":"2021-06-15T07:29:20.811073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"standings\",0) \ntmpdf.head(5)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.813320Z","iopub.execute_input":"2021-06-15T07:29:20.813663Z","iopub.status.idle":"2021-06-15T07:29:20.858262Z","shell.execute_reply.started":"2021-06-15T07:29:20.813632Z","shell.execute_reply":"2021-06-15T07:29:20.857634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* season : \n* gameDate : \n* divisionId  : このチームが所属する部門を表す一意識別子。\n* teamId  :   チームの一意の識別子。\n* teamName : \n* streakCode  : チームの現在の勝ち負けの連続の略語。最初の文字は勝ち負けを示し、数字はゲームの数です。\n* divisionRank  :  チームの部門における現在のランク。\n* leagueRank  :  リーグでのチームの現在のランク。\n* wildCardRank  :  ワイルドカードバースのチームの現在のランク。\n* leagueGamesBack  :  ゲームはチームのリーグに戻ります。\n* sportGamesBack  :  MLBのすべてに戻ってゲーム。\n* divisionGamesBack  :  チームの部門にゲームが戻ってきました。\n* wins  :  現在の勝利。\n* losses  :  現在の損失。\n* pct  :  現在の勝率。\n* runsAllowed  :  シーズン中に許可された実行。\n* runsScored  :  シーズンに得点したラン。\n* divisionChamp  :  チームが部門タイトルを獲得した場合はtrue。\n* divisionLeader  :  チームがディビジョンレースをリードしている場合はtrue。\n* wildCardLeader  :  チームがワイルドカードリーダーの場合はtrue。\n* eliminationNumber  :  ディビジョンレースから排除されるまでのゲーム数（チームの敗北+対戦相手の勝利）。\n* wildCardEliminationNumber  :  ワイルドカードレースから排除されるまでのゲーム数（チームの敗北+対戦相手の勝利）。\n* homeWins  :  ホームはシーズンに勝ちます。\n* homeLosses  :  シーズン中のホームロス。\n* awayWins  :  アウェイはシーズンに勝ちます。\n* awayLosses  :  シーズンのアウェイロス。\n* lastTenWins  :  過去10試合で勝ちました。\n* lastTenLosses  :  過去10試合で負けました。\n* extraInningWins  :  シーズンの追加イニングで勝ちます。\n* extraInningLosses  :  シーズンの追加イニングでの損失。\n* oneRunWins  :  シーズン中に1ランで勝ちます。\n* oneRunLosses  :  シーズン中に1ランで負けます。\n* dayWins  :  デイゲームはシーズンに勝ちます。\n* dayLosses Day game losses on the season. : \n* nightWins  : ナイトゲームはシーズンに勝ちます。\n* nightLosses  :   シーズン中のナイトゲームの敗北。\n* grassWins  :   芝生のフィールドがシーズンに勝ちます。\n* grassLosses  :   季節の草地の損失。\n* turfWins  :   芝フィールドはシーズンに勝ちます。\n* turfLosses  :   シーズン中の芝フィールドの損失。\n* divWins  :   シーズン中にディビジョンの対戦相手に勝ちます。\n* divLosses  :   シーズン中のディビジョンの対戦相手に対する敗北。\n* alWins  :   シーズン中にALチームに勝ちます。\n* alLosses  :   シーズン中のALチームに対する敗北。\n* nlWins  :   シーズン中にNLチームに勝ちます。\n* nlLosses  :   シーズン中のNLチームに対する敗北。\n* xWinLossPct  :   スコアリングおよび許可されたランに基づく予想勝率.","metadata":{"_kg_hide-input":true}},{"cell_type":"markdown","source":"## awards(train.csvのcolumn8番目)\n特定の日に配られたすべての賞または栄誉を含むネストされた JSON。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.859084Z","iopub.execute_input":"2021-06-15T07:29:20.859295Z","iopub.status.idle":"2021-06-15T07:29:20.883395Z","shell.execute_reply.started":"2021-06-15T07:29:20.859273Z","shell.execute_reply":"2021-06-15T07:29:20.882470Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"awards\",0) \ntmpdf","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.884433Z","iopub.execute_input":"2021-06-15T07:29:20.884677Z","iopub.status.idle":"2021-06-15T07:29:20.908534Z","shell.execute_reply.started":"2021-06-15T07:29:20.884652Z","shell.execute_reply":"2021-06-15T07:29:20.907695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* awardId : \n* awardName : \n* awardDate  : 日付賞が与えられました。\n* awardSeason  :   シーズンアワードはからでした。\n* playerId  :   プレーヤーの一意の識別子。\n* playerName : \n* awardPlayerTeamId : ","metadata":{}},{"cell_type":"markdown","source":"## events(train.csvのcolumn9番目)\n特定の日のすべてのオンフィールド ゲーム イベントを含むネストされた JSON。レギュラーシーズンとポストシーズンの試合が含まれます。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.911268Z","iopub.execute_input":"2021-06-15T07:29:20.911546Z","iopub.status.idle":"2021-06-15T07:29:20.937748Z","shell.execute_reply.started":"2021-06-15T07:29:20.911516Z","shell.execute_reply":"2021-06-15T07:29:20.936685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"events\",0) \ntmpdf.head(5)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:20.940556Z","iopub.execute_input":"2021-06-15T07:29:20.940835Z","iopub.status.idle":"2021-06-15T07:29:21.152054Z","shell.execute_reply.started":"2021-06-15T07:29:20.940807Z","shell.execute_reply":"2021-06-15T07:29:21.151086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* gamePk  : ゲームの一意の識別子。\n* gameDate : \n* gameTimeUTC  : UTCでの始球式。\n* season : \n* gameType  : ゲームの種類、さまざまな種類がここにあります。\n* playId  :  スタットキャストのプレイガイド。\n* eventId : \n* inning  : イニングABが発生しました。\n* halfInning :   「上」または「下」のイニングインジケーター。\n* homeScore  :   イベント開始時のホームスコア。\n* awayScore  :   イベント開始時のアウェイスコア。\n* menOnBase  :   走者がベースにいる場合に使用されるスプリット–すなわち（RISP、空）。\n* atBatIndex  :   で\n* atBatDesc  :   演奏する\n* atBatEvent  :   atBatのイベントタイプの結果。さまざまなタイプがここにあります。\n* hasOut  :   バイナリ、ランナーが場に出ている場合は1。\n* pitcherTeamId  :   ピッチングチームの一意の識別子。\n* isPitcherHome  :   バイナリ、投手がホームチームの場合は1。\n* pitcherTeam  :   ピッチングチームのチーム名。\n* hitterTeamId  :   打撃チームの一意の識別子。\n* hitterTeam  :   打撃チームのチーム名。\n* pitcherId : \n* pitcherName : \n* isStarter  : バイナリ、プレーヤーがゲームの先発投手だった場合は1。\n* pitcherHand  :   プレーヤーが手を投げる：「L」、「R」。\n* hitterId : \n* hitterName : \n* batSide  : プレーヤーのバット側：「L」、「R」。\n* pitchNumber  :  ABのピッチシーケンス番号。\n* balls  :  イベント後のボール数。\n* strikes  :  イベント後のストライクカウント。\n* isGB  :  バイナリ、打席がグラウンドボールの場合は1。\n* isLD  :  バイナリ、打席がラインドライブの場合は1。\n* isFB  :  バイナリ、打席が飛球の場合は1。\n* isPU  :  バイナリ、打席がポップアップの場合は1。\n* launchSpeed  :  打球の測定速度。\n* launchAngle  :  ヒットが開始された地平線に対する垂直角度。\n* totalDistance  :  ボールが移動した合計距離。\n* event  :  で発生する可能性のあるイベント\n* description  :  イベントのテキスト説明。\n* rbi  :  AB中に打点を打った回数。\n* pitchType  :  ピッチタイプ分類コード。さまざまなタイプがここにあります。\n* call  :  投球または投球の結果分類コード。さまざまなタイプがここにあります。\n* outs  :  ABの現在/最終アウト。\n* inPlay  :  ボールが場に出た場合は真/偽。\n* isPaOver  :  バイナリ、このイベントがプレートの外観の終わりである場合は1。\n* startSpeed  :  ホームプレートの前50フィートでのボールのMPHでの速度。\n* endSpeed  :  ボールがホームプレートの前端（x軸で0,0）を横切るときのボールのMPHでの速度。\n* nastyFactor  :  各ピッチのいくつかのプロパティを評価し、ピッチの「不快感」を0からのスケールで評価します\n* breakAngle  :  ピッチの平面が垂直から外れる時計回り（打者の視点）の角度。\n* breakLength  :  ピッチがピッチ開始とピッチ終了の間の直線から離れる最大距離。\n* breakY  :  ブレークが最大のホームプレートからの距離。\n* spinRate  :  ピッチャーによってRPMでリリースされた後のボールのスピン率。\n* spinDirection  :  スピンがボールの弾道にどのように影響するかを反映する角度として与えられる、リリース時のボールの回転軸。ピュアバック\n* pX  :  ボールがホームプレートの前軸と交差するときのボールのフィート単位の水平位置。\n* pZ  :  ボールがホームプレートの前軸と交差するときの、ボールのホームプレートからのフィート単位の垂直位置。\n* aX  :  z軸のボール加速度。\n* aY  :  y軸のボール加速度。\n* aZ  :  z 軸上のボールの加速度。\n* pfxX  :  インチ単位のボールの水平方向の動き。\n* pfxZ  :  インチ単位のボールの垂直方向の動き。\n* vX0  :  x軸からのボールの速度。\n* vY0  :  y軸からのボールの速度。 0,0,0 はバッターの後ろにあり、ボールはピッチャー マウンドから 0,0,0 に向かって移動するため、これは負です。\n* vZ0  :  z軸からのボールの速度。\n* x  :  ピッチがホームプレートの前を横切ったX座標。\n* y  :  ピッチがホームプレートの前面と交差するY座標。\n* x0  :  ピッチャーの手を離したときのボールの x 軸上の座標位置 (時間 = 0)。\n* y0  :  y軸上でピッチャーの手からボールがリリースされたポイントでのボールの座標位置（時間= 0）。\n* z0  :  z軸上でピッチャーの手からボールがリリースされたポイントでのボールの座標位置（時間= 0）。\n* type  :  「ピッチ」または「アクション」のいずれかのイベントのタイプ\n* zone  :  ゾーンロケーション番号.下を参照\n\n![image.png](attachment:1ad951bc-0f08-4424-83c4-6ff88a557d7d.png)\n","metadata":{"_kg_hide-input":true},"attachments":{"1ad951bc-0f08-4424-83c4-6ff88a557d7d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## playerTwitterFollowers(train.csvのcolumn10番目)\nその日の一部のプレイヤーの Twitter フォロワー数を含むネストされた JSON。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.153374Z","iopub.execute_input":"2021-06-15T07:29:21.153772Z","iopub.status.idle":"2021-06-15T07:29:21.176925Z","shell.execute_reply.started":"2021-06-15T07:29:21.153728Z","shell.execute_reply":"2021-06-15T07:29:21.175666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"playerTwitterFollowers\",0) \ntmpdf.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.178740Z","iopub.execute_input":"2021-06-15T07:29:21.179118Z","iopub.status.idle":"2021-06-15T07:29:21.209379Z","shell.execute_reply.started":"2021-06-15T07:29:21.179077Z","shell.execute_reply":"2021-06-15T07:29:21.208721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Twitterのフォローデータは、MLBによってメジャーリーグプレーヤーのTwitter APIから毎月1日に収集され、2018年1月1日までさかのぼります。 すべてのプレーヤーがTwitterアカウントを持っている/持っているわけではない、プレーヤーがランダムにアカウントを作成/削除/復元する、または特定の日にフォロワーデータを収集できないその他のシナリオがあるため、このデータセットはすべての月にわたってすべてのプレーヤーを網羅しているわけではありません。","metadata":{}},{"cell_type":"markdown","source":"* date  : フォロワー数の日付。\n* playerId  :   プレーヤーの一意の識別子。\n* playerName : \n* accountName  : プレイヤーのTwitterアカウントの名前。\n* twitterHandle  :   プレイヤーのツイッターハンドル。\n* numberOfFollowers  :   フォロワー数","metadata":{}},{"cell_type":"markdown","source":"## teamTwitterFollowers(train.csvのcolumn11番目)\nその日の各チームの Twitter フォロワー数を含むネストされた JSON。","metadata":{}},{"cell_type":"code","source":"training.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.210450Z","iopub.execute_input":"2021-06-15T07:29:21.210677Z","iopub.status.idle":"2021-06-15T07:29:21.233214Z","shell.execute_reply.started":"2021-06-15T07:29:21.210653Z","shell.execute_reply":"2021-06-15T07:29:21.232056Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tmpdf = exshow(\"teamTwitterFollowers\",0) \ntmpdf.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.234824Z","iopub.execute_input":"2021-06-15T07:29:21.235288Z","iopub.status.idle":"2021-06-15T07:29:21.262143Z","shell.execute_reply.started":"2021-06-15T07:29:21.235247Z","shell.execute_reply":"2021-06-15T07:29:21.261298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Twitterのフォローデータは、2018年1月1日までさかのぼって、毎月1日に、メジャーリーグの30チームすべてのTwitterAPIからMLBによって収集されました。","metadata":{}},{"cell_type":"markdown","source":"* date  : フォロワー数の日付。\n* teamId  :   チームの一意の識別子。\n* teamName : \n* accountName  : チームのTwitterアカウントの名前。\n* twitterHandle  :   チームのツイッターハンドル。","metadata":{}},{"cell_type":"markdown","source":"# 他のデータ ( awards.csv, players.csv, seasons.csv, teams.csv)","metadata":{}},{"cell_type":"markdown","source":"## starterにあったwidgetの練習(こんなことできるんだーと思いましたので・・・)","metadata":{}},{"cell_type":"code","source":"df_names = ['seasons', 'teams', 'players', 'awards']\n\npath = \"../input/mlb-player-digital-engagement-forecasting\"","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.263422Z","iopub.execute_input":"2021-06-15T07:29:21.263799Z","iopub.status.idle":"2021-06-15T07:29:21.270745Z","shell.execute_reply.started":"2021-06-15T07:29:21.263758Z","shell.execute_reply":"2021-06-15T07:29:21.269978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kaggle_data_tabs = widgets.Tab()\n# widgetsにそれぞれのDataFrameをchildrenの中にタブで表示\nkaggle_data_tabs.children = list([widgets.Output() for df_name in df_names])","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.271762Z","iopub.execute_input":"2021-06-15T07:29:21.272208Z","iopub.status.idle":"2021-06-15T07:29:21.301460Z","shell.execute_reply.started":"2021-06-15T07:29:21.272180Z","shell.execute_reply":"2021-06-15T07:29:21.300401Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for index in range(len(df_names)):\n    # タブのタイトルを設定\n    kaggle_data_tabs.set_title(index, df_names[index])\n    \n    df = pd.read_csv(os.path.join(path,df_names[index]) + \".csv\")\n    \n    # それぞれのタブにDataFrameを埋め込む\n    with kaggle_data_tabs.children[index]:\n        display(df)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.303106Z","iopub.execute_input":"2021-06-15T07:29:21.303482Z","iopub.status.idle":"2021-06-15T07:29:21.451593Z","shell.execute_reply.started":"2021-06-15T07:29:21.303442Z","shell.execute_reply":"2021-06-15T07:29:21.450717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(kaggle_data_tabs)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.452627Z","iopub.execute_input":"2021-06-15T07:29:21.452884Z","iopub.status.idle":"2021-06-15T07:29:21.460039Z","shell.execute_reply.started":"2021-06-15T07:29:21.452859Z","shell.execute_reply":"2021-06-15T07:29:21.459446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"-----------細かく一つ一つ見ていきます-----------","metadata":{}},{"cell_type":"markdown","source":"## 2.2 Seasons.csv","metadata":{}},{"cell_type":"code","source":"seasons = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/seasons.csv\")\nseasons","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.461158Z","iopub.execute_input":"2021-06-15T07:29:21.461394Z","iopub.status.idle":"2021-06-15T07:29:21.489046Z","shell.execute_reply.started":"2021-06-15T07:29:21.461369Z","shell.execute_reply":"2021-06-15T07:29:21.487982Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* seasonId : シーズンID\n* seasonStartDate : シーズンスタート日\n* seasonEndDate : シーズン終了日\n* preSeasonStartDate : 1つ前のシーズンスタート日\n* preSeasonEndDate : 1つ前のシーズンの終わりの日\n* regularSeasonStartDate : レギュラーシーズンのスタートの日\n* regularSeasonEndDate : レギュラーシーズンの終わりの日\n* lastDate1stHalf : 1st halfの最終日\n* allStarDate : オールスター戦の日付\n* firstDate2ndHalf : 2nd halfの始まり日\n* postSeasonStartDate : 次のシーズンのスタート日\n* postSeasonEndDate : 次のシーズンの終わり日","metadata":{}},{"cell_type":"markdown","source":"## 2.3 teams.csv","metadata":{}},{"cell_type":"code","source":"teams = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/teams.csv\")\nteams.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.490598Z","iopub.execute_input":"2021-06-15T07:29:21.491018Z","iopub.status.idle":"2021-06-15T07:29:21.510091Z","shell.execute_reply.started":"2021-06-15T07:29:21.490972Z","shell.execute_reply":"2021-06-15T07:29:21.509022Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## teams.csv\n* id - チームID\n* name : 名前\n* teamName : チームの名前\n* teamCode : チームのコード\n* shortName : 短い名前\n* abbreviation : 略語\n* locationName : 場所の名前\n* leagueId : リーグのid\n* leagueName : リーグの名前\n* divisionId : 部門id\n* divisionName : 部門名\n* venueId : 会場id\n* venueName : 会場名","metadata":{}},{"cell_type":"markdown","source":"## 2.4 players.csv","metadata":{}},{"cell_type":"code","source":"players = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/players.csv\")\nplayers.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.511435Z","iopub.execute_input":"2021-06-15T07:29:21.511734Z","iopub.status.idle":"2021-06-15T07:29:21.536446Z","shell.execute_reply.started":"2021-06-15T07:29:21.511688Z","shell.execute_reply":"2021-06-15T07:29:21.535783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* playerId - Unique identifier for a player. : プレーヤーID-プレーヤーの一意の識別子。\n* playerName : プレーヤの名前\n* DOB - Player’s date of birth. : DOB-プレーヤーの生年月日。\n* mlbDebutDate : MLBデビュー日\n* birthCity : 生まれた町\n* birthStateProvince : 出生州\n* birthCountry : 生まれた国\n* heightInches : 身長(inch)\n* weight : 体重\n* primaryPositionCode - Player’s primary position code : 主要ポジションコード\n* primaryPositionName - player’s primary position : 主要ポジション名\n* playerForTestSetAndFuturePreds - Boolean, true if player is among those for whom predictions are to be made in test data\n\n: **ブール値、プレーヤーがテストデータで予測が行われる対象の1人である場合はtrue**","metadata":{}},{"cell_type":"markdown","source":"## 2.5 awards.csv","metadata":{}},{"cell_type":"code","source":"awards = pd.read_csv(\"../input/mlb-player-digital-engagement-forecasting/awards.csv\")\nawards.head(3)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.537399Z","iopub.execute_input":"2021-06-15T07:29:21.537784Z","iopub.status.idle":"2021-06-15T07:29:21.562480Z","shell.execute_reply.started":"2021-06-15T07:29:21.537753Z","shell.execute_reply":"2021-06-15T07:29:21.561793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"このファイルには、日次データの開始前（つまり、2018年以前）にトレーニングセットのプレーヤーが獲得した賞が含まれています。\n\n* awardDate - Date award was given. : 授与日 - 授与された日付。\n* awardSeason - Season award was from. : アワードシーズン-シーズンアワードはからでした。\n* awardId : アワードid\n* awardName : アワード名\n* playerId - Unique identifier for a player. : プレーヤーID-プレーヤーの一意の識別子。\n* playerName : プレーヤーの名前\n* awardPlayerTeamId : プレイヤーのチームID","metadata":{}},{"cell_type":"markdown","source":"# 3. Data Merge","metadata":{}},{"cell_type":"markdown","source":"とりあえず、スターターhttps://www.kaggle.com/ryanholbrook/getting-started-with-mlb-player-digital-engagement　\n\nのコピーです。けっこう時間かかります。","metadata":{}},{"cell_type":"code","source":"df_names","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.563359Z","iopub.execute_input":"2021-06-15T07:29:21.563836Z","iopub.status.idle":"2021-06-15T07:29:21.568552Z","shell.execute_reply.started":"2021-06-15T07:29:21.563798Z","shell.execute_reply":"2021-06-15T07:29:21.567674Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for name in df_names:\n    globals()[name] = pd.read_csv(os.path.join(path,name)+ \".csv\")","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.569914Z","iopub.execute_input":"2021-06-15T07:29:21.570204Z","iopub.status.idle":"2021-06-15T07:29:21.606400Z","shell.execute_reply.started":"2021-06-15T07:29:21.570178Z","shell.execute_reply":"2021-06-15T07:29:21.605490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Unnest various nested data within training (daily) data ####","metadata":{}},{"cell_type":"code","source":"# 引数の　data:データとなる部分を指定します。辞書の中にSeries,配列,値もしくはリストに相当するオブジェクトを含むことができます\ndaily_data_unnested_dfs = pd.DataFrame(data = {\n  'dfName': training.drop('date', axis = 1).columns.values.tolist()\n  })\ndaily_data_unnested_dfs","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.607584Z","iopub.execute_input":"2021-06-15T07:29:21.607877Z","iopub.status.idle":"2021-06-15T07:29:21.618357Z","shell.execute_reply.started":"2021-06-15T07:29:21.607848Z","shell.execute_reply":"2021-06-15T07:29:21.617337Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ただの文法の確認だよ！\n# df.iterrows（）の動きを確認してるよ！\n# # iterrows()メソッドを使うと、1行ずつ、インデックス名（行名）とその行のデータ（pandas.Series型）のタプル(index, Series)を取得できる。\nfor index, series in daily_data_unnested_dfs.iterrows():\n    print(\"index:\",index)\n    print(\"series:\",series)\n    # 下記のおかげで、dfNameの各項目のみ取得できる（上から順に）\n    print(\"動きの確認:\",str(series['dfName']))\n    ","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.619683Z","iopub.execute_input":"2021-06-15T07:29:21.620057Z","iopub.status.idle":"2021-06-15T07:29:21.648854Z","shell.execute_reply.started":"2021-06-15T07:29:21.620018Z","shell.execute_reply":"2021-06-15T07:29:21.647974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"daily_data_unnested_dfs['df'] = [pd.DataFrame() for row in \n  daily_data_unnested_dfs.iterrows()]\ndaily_data_unnested_dfs","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.649980Z","iopub.execute_input":"2021-06-15T07:29:21.650222Z","iopub.status.idle":"2021-06-15T07:29:21.673231Z","shell.execute_reply.started":"2021-06-15T07:29:21.650197Z","shell.execute_reply":"2021-06-15T07:29:21.672046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for df_index, df_row in daily_data_unnested_dfs.iterrows():\n    nestedTableName = str(df_row['dfName'])\n    \n    # dateframeの任意の列を複数とるときにはdf[[A,B]]のようになるよ\n    date_nested_table = training[['date', nestedTableName]]\n    \n    # 「~」はnotという意味\n    # ~pd.isna　⇨naではないという意味になる\n    # ここでやっていることは、date_nested_tableの[nestedTableName]列のnaでないやつを新たな date_nested_tableとして定義している\n    date_nested_table = (date_nested_table[\n      ~pd.isna(date_nested_table[nestedTableName])\n      ].\n      reset_index(drop = True)\n      )\n    \n    # print(\"-------念のため　date_nested_tableの中身を確認してみる------\")\n    # print(date_nested_table)\n    # print(\"--------確認終わりだよ---------\")\n    \n    daily_dfs_collection = []\n    \n    for date_index, date_row in date_nested_table.iterrows():\n        daily_df = unpack_json(date_row[nestedTableName])\n        # print(\"-------念のため　date_indexの中身を確認してみる------\")\n        # print(date_index)\n        # print(\"--------確認終わりだよ---------\")\n        \n        # print(\"-------念のため　date_rowの中身を確認してみる------\")\n        # print(date_row)\n        # print(\"--------確認終わりだよ---------\")\n        \n        \n        # print(\"-------念のため　daily_dfの中身を確認してみる------\")\n        # print(daily_df)\n        # print(\"--------確認終わりだよ---------\")\n        \n        daily_df['dailyDataDate'] = date_row['date']\n        \n        daily_dfs_collection = daily_dfs_collection + [daily_df]\n    \n    # set_index()メソッドを使うとpandas.DataFrameの既存の列をインデックスindex（行名、行ラベル）に割り当てることができる。\n    # reset_index()メソッドを使うと、pandas.DataFrame, pandas.Seriesのインデックスindex（行名、行ラベル）を0始まりの連番（行番号）に振り直すことができる。\n    unnested_table = pd.concat(daily_dfs_collection,\n      ignore_index = True).set_index('dailyDataDate').reset_index()\n    \n    # print(\"-------念のため　unnested_tableの中身を確認してみる------\")\n    # print(unnested_table)\n    # print(\"--------確認終わりだよ---------\")\n\n    # Creates 1 pandas df per unnested df from daily data read in, with same name\n    # globals()関数とは、グローバル名前空間にあるシンボル一覧を返します。グローバル変数はモジュール(スクリプト)全体で有効な変数です。\n    globals()[df_row['dfName']] = unnested_table    \n    \n    daily_data_unnested_dfs['df'][df_index] = unnested_table","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:29:21.676018Z","iopub.execute_input":"2021-06-15T07:29:21.676302Z","iopub.status.idle":"2021-06-15T07:32:51.723698Z","shell.execute_reply.started":"2021-06-15T07:29:21.676271Z","shell.execute_reply":"2021-06-15T07:32:51.722659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 中身をいろいろ見てみる\ndaily_data_unnested_dfs","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:51.724893Z","iopub.execute_input":"2021-06-15T07:32:51.725150Z","iopub.status.idle":"2021-06-15T07:32:56.146120Z","shell.execute_reply.started":"2021-06-15T07:32:51.725125Z","shell.execute_reply":"2021-06-15T07:32:56.145217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"daily_data_unnested_dfs['df']","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:56.147159Z","iopub.execute_input":"2021-06-15T07:32:56.147408Z","iopub.status.idle":"2021-06-15T07:32:58.338396Z","shell.execute_reply.started":"2021-06-15T07:32:56.147383Z","shell.execute_reply":"2021-06-15T07:32:58.337312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"daily_data_unnested_dfs['df'][df_index]","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.339514Z","iopub.execute_input":"2021-06-15T07:32:58.339831Z","iopub.status.idle":"2021-06-15T07:32:58.359248Z","shell.execute_reply.started":"2021-06-15T07:32:58.339801Z","shell.execute_reply":"2021-06-15T07:32:58.358313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del training\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.360533Z","iopub.execute_input":"2021-06-15T07:32:58.360849Z","iopub.status.idle":"2021-06-15T07:32:58.469973Z","shell.execute_reply.started":"2021-06-15T07:32:58.360818Z","shell.execute_reply":"2021-06-15T07:32:58.468765Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" nextDayPlayerEngagement['dailyDataDate'].unique()","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.471550Z","iopub.execute_input":"2021-06-15T07:32:58.471974Z","iopub.status.idle":"2021-06-15T07:32:58.499329Z","shell.execute_reply.started":"2021-06-15T07:32:58.471931Z","shell.execute_reply":"2021-06-15T07:32:58.498112Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#### Get some information on each date in daily data (using season dates of interest) ####\ndates = pd.DataFrame(data = \n  {'dailyDataDate': nextDayPlayerEngagement['dailyDataDate'].unique()})\n\ndates['date'] = pd.to_datetime(dates['dailyDataDate'].astype(str))\n\ndates['year'] = dates['date'].dt.year\ndates['month'] = dates['date'].dt.month\ndates","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.500629Z","iopub.execute_input":"2021-06-15T07:32:58.501021Z","iopub.status.idle":"2021-06-15T07:32:58.544668Z","shell.execute_reply.started":"2021-06-15T07:32:58.500981Z","shell.execute_reply":"2021-06-15T07:32:58.543771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 結構前に定義したseasonsを確認する\nseasons","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.546528Z","iopub.execute_input":"2021-06-15T07:32:58.546852Z","iopub.status.idle":"2021-06-15T07:32:58.563627Z","shell.execute_reply.started":"2021-06-15T07:32:58.546820Z","shell.execute_reply":"2021-06-15T07:32:58.562689Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dates_with_info = pd.merge(\n  dates,\n  seasons,\n  left_on = 'year',\n  right_on = 'seasonId'\n  )\n\n# dates_with_infoのdateが両者の間に存在するか否か\n# \ndates_with_info['inSeason'] = (\n  dates_with_info['date'].between(\n    dates_with_info['regularSeasonStartDate'],\n    dates_with_info['postSeasonEndDate'],\n    inclusive = True\n    )\n  )\n\ndates_with_info","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.565015Z","iopub.execute_input":"2021-06-15T07:32:58.565562Z","iopub.status.idle":"2021-06-15T07:32:58.608263Z","shell.execute_reply.started":"2021-06-15T07:32:58.565521Z","shell.execute_reply":"2021-06-15T07:32:58.607306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# numpy.select(condlist, choicelist, default=0)\n# default = np.nanについては、条件が当てはまらない場合は、とりあえずnanを返す\ndates_with_info['seasonPart'] = np.select(\n  [\n    dates_with_info['date'] < dates_with_info['preSeasonStartDate'], \n    dates_with_info['date'] < dates_with_info['regularSeasonStartDate'],\n    dates_with_info['date'] <= dates_with_info['lastDate1stHalf'],\n    dates_with_info['date'] < dates_with_info['firstDate2ndHalf'],\n    dates_with_info['date'] <= dates_with_info['regularSeasonEndDate'],\n    dates_with_info['date'] < dates_with_info['postSeasonStartDate'],\n    dates_with_info['date'] <= dates_with_info['postSeasonEndDate'],\n    dates_with_info['date'] > dates_with_info['postSeasonEndDate']\n  ], \n  [\n    'Offseason',\n    'Preseason',\n    'Reg Season 1st Half',\n    'All-Star Break',\n    'Reg Season 2nd Half',\n    'Between Reg and Postseason',\n    'Postseason',\n    'Offseason'\n  ], \n  default = np.nan\n  )\n\n\ndates_with_info","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.609437Z","iopub.execute_input":"2021-06-15T07:32:58.609721Z","iopub.status.idle":"2021-06-15T07:32:58.649348Z","shell.execute_reply.started":"2021-06-15T07:32:58.609682Z","shell.execute_reply":"2021-06-15T07:32:58.648482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"playerBoxScores","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.650660Z","iopub.execute_input":"2021-06-15T07:32:58.650949Z","iopub.status.idle":"2021-06-15T07:32:58.981610Z","shell.execute_reply.started":"2021-06-15T07:32:58.650923Z","shell.execute_reply":"2021-06-15T07:32:58.980578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#### Add some pitching stats/pieces of info to player game level stats ####\n\n# とりあえず、player_game_statsにplayerBoxScoresのrenameした値を代入したよ\nplayer_game_stats = (playerBoxScores.copy().\n  # Change team Id/name to reflect these come from player game, not roster\n  rename(columns = {'teamId': 'gameTeamId', 'teamName': 'gameTeamName'})\n  )\n\nplayer_game_stats","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:58.982828Z","iopub.execute_input":"2021-06-15T07:32:58.983181Z","iopub.status.idle":"2021-06-15T07:32:59.425608Z","shell.execute_reply.started":"2021-06-15T07:32:58.983150Z","shell.execute_reply":"2021-06-15T07:32:59.424596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Adds in field for innings pitched as fraction (better for aggregation)\n# 野球のマニアックなことしてるよ。正直わかなくても大丈夫！文法自体はすごく簡単だよ。\n\n# numpy.where()は、条件式conditionを満たす場合（真Trueの場合）はx、満たさない場合（偽Falseの場合）はyとするndarrayを返す関数。\nplayer_game_stats['inningsPitchedAsFrac'] = np.where(\n  pd.isna(player_game_stats['inningsPitched']),\n  np.nan,\n  np.floor(player_game_stats['inningsPitched']) +\n    (player_game_stats['inningsPitched'] -\n      np.floor(player_game_stats['inningsPitched'])) * 10/3\n  )","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:59.426731Z","iopub.execute_input":"2021-06-15T07:32:59.426994Z","iopub.status.idle":"2021-06-15T07:32:59.441124Z","shell.execute_reply.started":"2021-06-15T07:32:59.426967Z","shell.execute_reply":"2021-06-15T07:32:59.440013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Add in Tom Tango pitching game score (https://www.mlb.com/glossary/advanced-stats/game-score)\n# 野球のマニアックなことしてるよ。正直わかなくても大丈夫！文法自体はすごく簡単だよ。\nplayer_game_stats['pitchingGameScore'] = (40\n#     + 2 * player_game_stats['outs']\n    + 1 * player_game_stats['strikeOutsPitching']\n    - 2 * player_game_stats['baseOnBallsPitching']\n    - 2 * player_game_stats['hitsPitching']\n    - 3 * player_game_stats['runsPitching']\n    - 6 * player_game_stats['homeRunsPitching'])","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:59.442520Z","iopub.execute_input":"2021-06-15T07:32:59.443119Z","iopub.status.idle":"2021-06-15T07:32:59.458213Z","shell.execute_reply.started":"2021-06-15T07:32:59.443077Z","shell.execute_reply":"2021-06-15T07:32:59.457324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Add in criteria for no-hitter by pitcher (individual, not multiple pitchers)\nplayer_game_stats['noHitter'] = np.where(\n  (player_game_stats['gamesStartedPitching'] == 1) &\n  (player_game_stats['inningsPitched'] >= 9) &\n  (player_game_stats['hitsPitching'] == 0),\n  1, 0\n  )","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:59.459990Z","iopub.execute_input":"2021-06-15T07:32:59.460305Z","iopub.status.idle":"2021-06-15T07:32:59.470261Z","shell.execute_reply.started":"2021-06-15T07:32:59.460276Z","shell.execute_reply":"2021-06-15T07:32:59.469619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"player_date_stats_agg = pd.merge(\n  (player_game_stats.\n    groupby(['dailyDataDate', 'playerId'], as_index = False).\n    # Some aggregations that are not simple sums\n    agg(\n      numGames = ('gamePk', 'nunique'),\n      # Should be 1 team per player per day, but adding here for 1 exception:\n      # playerId 518617 (Jake Diekman) had 2 games for different teams marked\n      # as played on 5/19/19, due to resumption of game after he was traded\n      numTeams = ('gameTeamId', 'nunique'),\n      # Should be only 1 team for almost all player-dates, taking min to simplify\n      gameTeamId = ('gameTeamId', 'min')\n      )\n    ),\n  # Merge with a bunch of player stats that can be summed at date/player level\n  (player_game_stats.\n    groupby(['dailyDataDate', 'playerId'], as_index = False)\n    [['runsScored', 'homeRuns', 'strikeOuts', 'baseOnBalls', 'hits',\n      'hitByPitch', 'atBats', 'caughtStealing', 'stolenBases',\n      'groundIntoDoublePlay', 'groundIntoTriplePlay', 'plateAppearances',\n      'totalBases', 'rbi', 'leftOnBase', 'sacBunts', 'sacFlies',\n      'gamesStartedPitching', 'runsPitching', 'homeRunsPitching', \n      'strikeOutsPitching', 'baseOnBallsPitching', 'hitsPitching',\n      'inningsPitchedAsFrac', 'earnedRuns', \n      'battersFaced','saves', 'blownSaves', 'pitchingGameScore', \n      'noHitter'\n      ]].\n    sum()\n    ),\n  on = ['dailyDataDate', 'playerId'],\n  how = 'inner'\n  )","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:32:59.471385Z","iopub.execute_input":"2021-06-15T07:32:59.471941Z","iopub.status.idle":"2021-06-15T07:33:00.025006Z","shell.execute_reply.started":"2021-06-15T07:32:59.471902Z","shell.execute_reply":"2021-06-15T07:33:00.024048Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 1つ1つのやっている内容はすごく簡単だよ！とりあえず中身理解してないけどまとめて実行するよ\n\n#### Turn games table into 1 row per team-game, then merge with team box scores ####\n# Filter to regular or Postseason games w/ valid scores for this part\ngames_for_stats = games[\n  np.isin(games['gameType'], ['R', 'F', 'D', 'L', 'W', 'C', 'P']) &\n  ~pd.isna(games['homeScore']) &\n  ~pd.isna(games['awayScore'])\n  ]\n\n# Get games table from home team perspective\ngames_home_perspective = games_for_stats.copy()\n\n# Change column names so that \"team\" is \"home\", \"opp\" is \"away\"\ngames_home_perspective.columns = [\n  col_value.replace('home', 'team').replace('away', 'opp') for \n    col_value in games_home_perspective.columns.values]\n\ngames_home_perspective['isHomeTeam'] = 1\n\n# Get games table from away team perspective\ngames_away_perspective = games_for_stats.copy()\n\n# Change column names so that \"opp\" is \"home\", \"team\" is \"away\"\ngames_away_perspective.columns = [\n  col_value.replace('home', 'opp').replace('away', 'team') for \n    col_value in games_away_perspective.columns.values]\n\ngames_away_perspective['isHomeTeam'] = 0\n\n# Put together games from home/away perspective to get df w/ 1 row per team game\nteam_games = (pd.concat([\n  games_home_perspective,\n  games_away_perspective\n  ],\n  ignore_index = True)\n  )\n\n# Copy over team box scores data to modify\nteam_game_stats = teamBoxScores.copy()\n\n# Change column names to reflect these are all \"team\" stats - helps \n# to differentiate from individual player stats if/when joining later\nteam_game_stats.columns = [\n  (col_value + 'Team') \n  if (col_value not in ['dailyDataDate', 'home', 'teamId', 'gamePk',\n    'gameDate', 'gameTimeUTC'])\n    else col_value\n  for col_value in team_game_stats.columns.values\n  ]\n\n# Merge games table with team game stats\nteam_games_with_stats = pd.merge(\n  team_games,\n  team_game_stats.\n    # Drop some fields that are already present in team_games table\n    drop(['home', 'gameDate', 'gameTimeUTC'], axis = 1),\n  on = ['dailyDataDate', 'gamePk', 'teamId'],\n  # Doing this as 'inner' join excludes spring training games, postponed games,\n  # etc. from original games table, but this may be fine for purposes here \n  how = 'inner'\n  )\n\nteam_date_stats_agg = (team_games_with_stats.\n  groupby(['dailyDataDate', 'teamId', 'gameType', 'oppId', 'oppName'], \n    as_index = False).\n  agg(\n    numGamesTeam = ('gamePk', 'nunique'),\n    winsTeam = ('teamWinner', 'sum'),\n    lossesTeam = ('oppWinner', 'sum'),\n    runsScoredTeam = ('teamScore', 'sum'),\n    runsAllowedTeam = ('oppScore', 'sum')\n    )\n   )\n\n# Prepare standings table for merge w/ player digital engagement data\n# Pick only certain fields of interest from standings for merge\nstandings_selected_fields = (standings[['dailyDataDate', 'teamId', \n  'streakCode', 'divisionRank', 'leagueRank', 'wildCardRank', 'pct'\n  ]].\n  rename(columns = {'pct': 'winPct'})\n  )\n\n# Change column names to reflect these are all \"team\" standings - helps \n# to differentiate from player-related fields if/when joining later\nstandings_selected_fields.columns = [\n  (col_value + 'Team') \n  if (col_value not in ['dailyDataDate', 'teamId'])\n    else col_value\n  for col_value in standings_selected_fields.columns.values\n  ]\n\nstandings_selected_fields['streakLengthTeam'] = (\n  standings_selected_fields['streakCodeTeam'].\n    str.replace('W', '').\n    str.replace('L', '').\n    astype(float)\n    )\n\n# Add fields to separate winning and losing streak from streak code\nstandings_selected_fields['winStreakTeam'] = np.where(\n  standings_selected_fields['streakCodeTeam'].str[0] == 'W',\n  standings_selected_fields['streakLengthTeam'],\n  np.nan\n  )\n\nstandings_selected_fields['lossStreakTeam'] = np.where(\n  standings_selected_fields['streakCodeTeam'].str[0] == 'L',\n  standings_selected_fields['streakLengthTeam'],\n  np.nan\n  )\n\nstandings_for_digital_engagement_merge = (pd.merge(\n  standings_selected_fields,\n  dates_with_info[['dailyDataDate', 'inSeason']],\n  on = ['dailyDataDate'],\n  how = 'left'\n  ).\n  # Limit down standings to only in season version\n  query(\"inSeason\").\n  # Drop fields no longer necessary (in derived values, etc.)\n  drop(['streakCodeTeam', 'streakLengthTeam', 'inSeason'], axis = 1).\n  reset_index(drop = True)\n  )\n\n#### Merge together various data frames to add date, player, roster, and team info ####\n# Copy over player engagement df to add various pieces to it\nplayer_engagement_with_info = nextDayPlayerEngagement.copy()\n\n# Take \"row mean\" across targets to add (helps with studying all 4 targets at once)\nplayer_engagement_with_info['targetAvg'] = np.mean(\n  player_engagement_with_info[['target1', 'target2', 'target3', 'target4']],\n  axis = 1)\n\n# Merge in date information\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  dates_with_info[['dailyDataDate', 'date', 'year', 'month', 'inSeason',\n    'seasonPart']],\n  on = ['dailyDataDate'],\n  how = 'left'\n  )\n\n# Merge in some player information\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  players[['playerId', 'playerName', 'DOB', 'mlbDebutDate', 'birthCity',\n    'birthStateProvince', 'birthCountry', 'primaryPositionName']],\n   on = ['playerId'],\n   how = 'left'\n   )\n\n# Merge in some player roster information by date\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  (rosters[['dailyDataDate', 'playerId', 'statusCode', 'status', 'teamId']].\n    rename(columns = {\n      'statusCode': 'rosterStatusCode',\n      'status': 'rosterStatus',\n      'teamId': 'rosterTeamId'\n      })\n    ),\n  on = ['dailyDataDate', 'playerId'],\n  how = 'left'\n  )\n    \n# Merge in team name from player's roster team\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  (teams[['id', 'teamName']].\n    rename(columns = {\n      'id': 'rosterTeamId',\n      'teamName': 'rosterTeamName'\n      })\n    ),\n  on = ['rosterTeamId'],\n  how = 'left'\n  )\n\n# Merge in some player game stats (previously aggregated) from that date\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  player_date_stats_agg,\n  on = ['dailyDataDate', 'playerId'],\n  how = 'left'\n  )\n\n# Merge in team name from player's game team\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  (teams[['id', 'teamName']].\n    rename(columns = {\n      'id': 'gameTeamId',\n      'teamName': 'gameTeamName'\n      })\n    ),\n  on = ['gameTeamId'],\n  how = 'left'\n  )\n\n# Merge in some team game stats/results (previously aggregated) from that date\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  team_date_stats_agg.rename(columns = {'teamId': 'gameTeamId'}),\n  on = ['dailyDataDate', 'gameTeamId'],\n  how = 'left'\n  )\n\n# Merge in player transactions of note on that date\n    \n# Merge in some pieces of team standings (previously filter/processed) from that date\nplayer_engagement_with_info = pd.merge(\n  player_engagement_with_info,\n  standings_for_digital_engagement_merge.\n    rename(columns = {'teamId': 'gameTeamId'}),\n  on = ['dailyDataDate', 'gameTeamId'],\n  how = 'left'\n  )\n\ndisplay(player_engagement_with_info)","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:33:00.026461Z","iopub.execute_input":"2021-06-15T07:33:00.026766Z","iopub.status.idle":"2021-06-15T07:33:11.809563Z","shell.execute_reply.started":"2021-06-15T07:33:00.026736Z","shell.execute_reply":"2021-06-15T07:33:11.808647Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"player_engagement_with_info.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:33:11.814090Z","iopub.execute_input":"2021-06-15T07:33:11.814358Z","iopub.status.idle":"2021-06-15T07:33:11.829620Z","shell.execute_reply.started":"2021-06-15T07:33:11.814331Z","shell.execute_reply":"2021-06-15T07:33:11.828796Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"output結果をreferenceできるように、一応pickleで保存しておきます。","metadata":{}},{"cell_type":"code","source":"player_engagement_with_info.to_pickle(\"player_engagement_with_info.pkl\")","metadata":{"execution":{"iopub.status.busy":"2021-06-15T07:33:11.831333Z","iopub.execute_input":"2021-06-15T07:33:11.831617Z","iopub.status.idle":"2021-06-15T07:33:20.955372Z","shell.execute_reply.started":"2021-06-15T07:33:11.831590Z","shell.execute_reply":"2021-06-15T07:33:20.954265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}