├── README.md ├── [Qiita]Python初学者のためのPandas100本ノック.url ├── input ├── a_000.txt ├── a_001.txt ├── a_002.txt ├── a_003.txt ├── a_004.txt ├── a_005.txt ├── a_006.txt ├── a_007.txt ├── a_008.txt ├── a_009.txt ├── a_010.txt ├── a_011.txt ├── a_012.txt ├── a_013.txt ├── a_014.txt ├── a_015.txt ├── a_016.txt ├── a_017.txt ├── a_018.txt ├── a_019.txt ├── a_020.txt ├── a_021.txt ├── a_022.txt ├── a_023.txt ├── a_024.txt ├── a_025.txt ├── a_026.txt ├── a_027.txt ├── a_028.txt ├── a_029.txt ├── a_030.txt ├── a_031.txt ├── a_032.txt ├── a_033.txt ├── a_034.txt ├── a_035.txt ├── a_036.txt ├── a_037.txt ├── a_038.txt ├── a_039.txt ├── a_040.txt ├── a_041.txt ├── a_042.txt ├── a_043.txt ├── a_044.txt ├── a_045.txt ├── a_046.txt ├── a_047.txt ├── a_048.txt ├── a_049.txt ├── a_050.txt ├── a_051.txt ├── a_052.txt ├── a_053.txt ├── a_054.txt ├── a_055.txt ├── a_056.txt ├── a_057.txt ├── a_058.txt ├── a_059.txt ├── a_060.txt ├── a_061.txt ├── a_062.txt ├── a_063.txt ├── a_064.txt ├── a_065.txt ├── a_066.txt ├── a_067.txt ├── a_068.txt ├── a_069.txt ├── a_070.txt ├── a_071.txt ├── a_072.txt ├── a_073.txt ├── a_074.txt ├── a_075.txt ├── a_076.txt ├── a_077.txt ├── a_078.txt ├── a_079.txt ├── a_080.txt ├── a_081.txt ├── a_082.txt ├── a_083.txt ├── a_084.txt ├── a_085.txt ├── a_086.txt ├── a_087.txt ├── a_088.txt ├── a_089.txt ├── a_090.txt ├── a_091.txt ├── a_092.txt ├── a_093.txt ├── a_094.txt ├── a_095.txt ├── a_096.txt ├── a_097.txt ├── a_098.txt ├── a_099.txt ├── a_100.txt ├── data1.csv ├── data1_2.csv ├── data1_3.csv ├── data2.csv ├── q_000.txt ├── q_001.txt ├── q_002.txt ├── q_003.txt ├── q_004.txt ├── q_005.txt ├── q_006.txt ├── q_007.txt ├── q_008.txt ├── q_009.txt ├── q_010.txt ├── q_011.txt ├── q_012.txt ├── q_013.txt ├── q_014.txt ├── q_015.txt ├── q_016.txt ├── q_017.txt ├── q_018.txt ├── q_019.txt ├── q_020.txt ├── q_021.txt ├── q_022.txt ├── q_023.txt ├── q_024.txt ├── q_025.txt ├── q_026.txt ├── q_027.txt ├── q_028.txt ├── q_029.txt ├── q_030.txt ├── q_031.txt ├── q_032.txt ├── q_033.txt ├── q_034.txt ├── q_035.txt ├── q_036.txt ├── q_037.txt ├── q_038.txt ├── q_039.txt ├── q_040.txt ├── q_041.txt ├── q_042.txt ├── q_043.txt ├── q_044.txt ├── q_045.txt ├── q_046.txt ├── q_047.txt ├── q_048.txt ├── q_049.txt ├── q_050.txt ├── q_051.txt ├── q_052.txt ├── q_053.txt ├── q_054.txt ├── q_055.txt ├── q_056.txt ├── q_057.txt ├── q_058.txt ├── q_059.txt ├── q_060.txt ├── q_061.txt ├── q_062.txt ├── q_063.txt ├── q_064.txt ├── q_065.txt ├── q_066.txt ├── q_067.txt ├── q_068.txt ├── q_069.txt ├── q_070.txt ├── q_071.txt ├── q_072.txt ├── q_073.txt ├── q_074.txt ├── q_075.txt ├── q_076.txt ├── q_077.txt ├── q_078.txt ├── q_079.txt ├── q_080.txt ├── q_081.txt ├── q_082.txt ├── q_083.txt ├── q_084.txt ├── q_085.txt ├── q_086.txt ├── q_087.txt ├── q_088.txt ├── q_089.txt └── titanic3.csv ├── notebook ├── 01_Pandas_100_Knocks_for_Begginer(all_answer_displayed).ipynb ├── 01_Pandas_100_Knocks_for_Begginer.ipynb ├── 02_Pandas_100_Knocks_for_Begginer_Random_Knocks.ipynb └── 03_Titanic_Passengers_Prediction.ipynb ├── output └── submission.csv └── pandas_100knocks_index_v1.0.3.xlsx /README.md: -------------------------------------------------------------------------------- 1 | #### ダウンロード方法 2 | 右上の「Code」→「Download ZIP」よりダウンロード 3 | 4 | #### Python初学者のためのPandas100本ノック 5 | https://qiita.com/kunishou/items/bd5fad9a334f4f5be51c 6 | -------------------------------------------------------------------------------- /[Qiita]Python初学者のためのPandas100本ノック.url: -------------------------------------------------------------------------------- 1 | [{000214A0-0000-0000-C000-000000000046}] 2 | Prop3=19,11 3 | [InternetShortcut] 4 | IDList= 5 | URL=https://qiita.com/kunishou/items/bd5fad9a334f4f5be51c 6 | -------------------------------------------------------------------------------- /input/a_000.txt: -------------------------------------------------------------------------------- 1 | [answer0] 2 | 3 | このように回答コード例がここに表示されます。 4 | 各問題の回答コード例を表示するときはprintの前の 5 | #を消して実行して下さい。 6 | 7 | コード例をセル内にコピー&ペーストし 8 | 実行すると、正解のデータが表示されます -------------------------------------------------------------------------------- /input/a_001.txt: -------------------------------------------------------------------------------- 1 | [answer1] 2 | 3 | df.head() 4 | 5 | 6 | [tips] 7 | .head()はデフォルトで先頭5行表示 8 | ()に表示したい行数を入れる 9 | 先頭10行表示は .head(10) -------------------------------------------------------------------------------- /input/a_002.txt: -------------------------------------------------------------------------------- 1 | [answer2] 2 | 3 | df.tail() 4 | 5 | 6 | [tips] 7 | .tail()はデフォルトで最後の5行表示 8 | ()に表示したい行数を入れる 9 | 最後10行表示は .tail(10) -------------------------------------------------------------------------------- /input/a_003.txt: -------------------------------------------------------------------------------- 1 | [answer3] 2 | 3 | df.shape -------------------------------------------------------------------------------- /input/a_004.txt: -------------------------------------------------------------------------------- 1 | [answer4] 2 | 3 | df2 = pd.read_csv('../input/data1.csv') 4 | df2.head() 5 | 6 | 7 | [Tips] 8 | ・csvの読み込みはread_csv 9 | ・必要に応じてencoding=''を指定する 10 | utf-8 11 | shift_jis (日本語) 12 | cp932 (Windows拡張文字含む日本語) 13 | 14 | ex) 15 | df2 = pd.read_csv('../input/data1.csv',encoding='utf-8') -------------------------------------------------------------------------------- /input/a_005.txt: -------------------------------------------------------------------------------- 1 | [answer5] 2 | 3 | df.sort_values('fare') 4 | 5 | 6 | [Tips] 7 | ・要素でソートするときはsort_valuesを使用 8 | ・デフォルトでは昇順 9 | ・降順でソートしたい場合は ascending=False を指定 10 | ・ソートする列を複数指定可能 11 | 12 | ex) 13 | 降順でソート 14 | df.sort_values('fare', ascending=False) 15 | 16 | 複数列でのソート 17 | df.sort_values(['fare','age']) -------------------------------------------------------------------------------- /input/a_006.txt: -------------------------------------------------------------------------------- 1 | [answer6] 2 | 3 | df_copy = df.copy() 4 | df_copy.head() 5 | 6 | 7 | [Tips] 8 | ① df_copy = df と ② df_copy = df.copy() では 9 | 挙動が異なるので注意が必要。 10 | 11 | ①の場合、df_copyはdfを参照しているだけのため、 12 | df側の値を変えると、df_copy側の値も変わる 13 | (dfとdf_copyは連動)。 14 | 15 | df側の値の変更をdf_copy側に反映させたくない 16 | 場合には②のcopy()を使う(dfとdf_copyは独立)。 -------------------------------------------------------------------------------- /input/a_007.txt: -------------------------------------------------------------------------------- 1 | [answer7] 2 | 3 | print(df.dtypes) 4 | print(df['cabin'].dtype) 5 | 6 | 7 | [Tips] 8 | ・DataFrameのすべての列のデータ型を確認したい場合は dtypes 9 | ・DataFrameの一つの列のデータ型を確認したい場合は dtype 10 | 11 | ※複数のコードの結果を表示したい場合、printを使用 12 | (printをつけない場合、1行目のdtypesの結果しか表示されない) -------------------------------------------------------------------------------- /input/a_008.txt: -------------------------------------------------------------------------------- 1 | [answer8] 2 | 3 | print(df['pclass'].dtype) 4 | df['pclass'] = df['pclass'].astype(str) 5 | print(df['pclass'].dtype) 6 | 7 | 8 | [Tips] 9 | ・データ型を変更する場合は astype を使用 10 | ・問題40の列同士の結合では、数値列と文字列を 11 | 結合しますが、データ型が異なると結合が上手く 12 | いきません。両方の列のデータ型が同じになるように 13 | astypeを使用してデータ型の変換をします。 -------------------------------------------------------------------------------- /input/a_009.txt: -------------------------------------------------------------------------------- 1 | [answer9] 2 | 3 | len(df) 4 | 5 | 6 | [Tips] 7 | dataframeのレコード数(行数)を知りたい時は 8 | len()を使用 -------------------------------------------------------------------------------- /input/a_010.txt: -------------------------------------------------------------------------------- 1 | [answer10] 2 | 3 | df.info() 4 | 5 | 6 | [Tips] 7 | ・レコード数(行数)、各列のデータ型、欠損値の有無の 8 | 　確認にはinfo()を使用 9 | ・RangeIndexがレコード数 10 | ・Data columnsがカラム数 11 | ・Non-Null Countがレコードが入ってる数 12 | ・今回、1309レコードだがcabinのNon-Null Countは 13 | 　295なので1309-295のレコードについては欠損している -------------------------------------------------------------------------------- /input/a_011.txt: -------------------------------------------------------------------------------- 1 | [answer11] 2 | 3 | print(df['sex'].unique()) 4 | print(df['cabin'].unique()) 5 | 6 | 7 | [Tips] 8 | 列に含まれる要素の確認にはunique()を使用 -------------------------------------------------------------------------------- /input/a_012.txt: -------------------------------------------------------------------------------- 1 | [answer12] 2 | 3 | df.columns.tolist() 4 | 5 | 6 | [Tips] 7 | ・列名を一覧表示するにはcolumnsを使用 8 | ・.tolist()を付けることでlist形式に変換 9 | ・ndarray形式で表示する場合は.valuesを使用 10 | 11 | df.columns.values -------------------------------------------------------------------------------- /input/a_013.txt: -------------------------------------------------------------------------------- 1 | [answer13] 2 | 3 | df.index.values 4 | 5 | 6 | [Tips] 7 | ・インデックスを一覧表示するには.indexを使用 8 | ・.valuesを付けることでndaaray形式に変換 9 | ・list形式で表示する場合はtolist()を使用 10 | 11 | df.index.tolist() -------------------------------------------------------------------------------- /input/a_014.txt: -------------------------------------------------------------------------------- 1 | [answer14] 2 | 3 | df['name'] -------------------------------------------------------------------------------- /input/a_015.txt: -------------------------------------------------------------------------------- 1 | [answer15] 2 | 3 | df[['name','sex']] -------------------------------------------------------------------------------- /input/a_016.txt: -------------------------------------------------------------------------------- 1 | [answer16] 2 | 3 | df[:4] -------------------------------------------------------------------------------- /input/a_017.txt: -------------------------------------------------------------------------------- 1 | [answer17] 2 | 3 | df[3:10] -------------------------------------------------------------------------------- /input/a_018.txt: -------------------------------------------------------------------------------- 1 | [answer18] 2 | 3 | df.loc[:,:] -------------------------------------------------------------------------------- /input/a_019.txt: -------------------------------------------------------------------------------- 1 | [answer19] 2 | 3 | df.loc[:,'fare'] -------------------------------------------------------------------------------- /input/a_020.txt: -------------------------------------------------------------------------------- 1 | [answer20] 2 | 3 | df.loc[:10,'fare'] -------------------------------------------------------------------------------- /input/a_021.txt: -------------------------------------------------------------------------------- 1 | [answer21] 2 | 3 | df.loc[:,['name','ticket']] -------------------------------------------------------------------------------- /input/a_022.txt: -------------------------------------------------------------------------------- 1 | [answer22] 2 | 3 | df.loc[:,'name':'cabin'] -------------------------------------------------------------------------------- /input/a_023.txt: -------------------------------------------------------------------------------- 1 | [answer23] 2 | 3 | df.iloc[:5,4] 4 | 5 | 6 | [tips] 7 | ilocは抽出する行、列を番号で指定 -------------------------------------------------------------------------------- /input/a_024.txt: -------------------------------------------------------------------------------- 1 | [answer24] 2 | 3 | df_copy = df[['name','age','sex']] 4 | df_copy.to_csv('../output/sample.csv') 5 | 6 | 7 | [Tips] 8 | ・to_csvでcsv形式で出力 9 | ・行番号、列名を削除して出力したいときは 10 | 　index=None,header=Noneをつける 11 | 12 | df_copy.to_csv('../output/sample.csv',index=None,header=None) -------------------------------------------------------------------------------- /input/a_025.txt: -------------------------------------------------------------------------------- 1 | [answer25] 2 | 3 | df[df['age'] >= 30] -------------------------------------------------------------------------------- /input/a_026.txt: -------------------------------------------------------------------------------- 1 | [answer26] 2 | 3 | df[df['sex'] == 'female'] -------------------------------------------------------------------------------- /input/a_027.txt: -------------------------------------------------------------------------------- 1 | [answer27] 2 | 3 | df[(df['sex'] == 'female' ) & (df['age'] >= 40)] -------------------------------------------------------------------------------- /input/a_028.txt: -------------------------------------------------------------------------------- 1 | [answer28] 2 | 3 | df.query('sex == "female" & age >= 40 ') 4 | -------------------------------------------------------------------------------- /input/a_029.txt: -------------------------------------------------------------------------------- 1 | [answer29] 2 | 3 | df.query('name.str.contains("Mrs")', engine='python') 4 | 5 | 6 | [Tips] 7 | 特定の文字列を含むデータを抽出したいときは 8 | str.contains()を使用 9 | engine='python'を指定しないとエラーが出る -------------------------------------------------------------------------------- /input/a_030.txt: -------------------------------------------------------------------------------- 1 | [answer30] 2 | 3 | df.select_dtypes(include='object') 4 | 5 | 6 | [Tips] 7 | ・特定のデータ型の列を抽出したい時は 8 | 　select_dtypesを使用する。 9 | ・今回は文字列を抽出したいのでinclude='object'を 10 | 　指定 11 | ・exclude='object'とすれば数値型の列を抽出可能 12 | 13 | ex) 14 | df.select_dtypes(exclude='object') -------------------------------------------------------------------------------- /input/a_031.txt: -------------------------------------------------------------------------------- 1 | [answer31] 2 | 3 | df.nunique() 4 | 5 | 6 | [Tips] 7 | ユニークな要素数の確認にはnunique()を使用 -------------------------------------------------------------------------------- /input/a_032.txt: -------------------------------------------------------------------------------- 1 | [answer32] 2 | 3 | df['embarked'].value_counts() 4 | 5 | 6 | [Tips] 7 | ユニークな要素と出現数を確認するには 8 | value_counts()を使用 -------------------------------------------------------------------------------- /input/a_033.txt: -------------------------------------------------------------------------------- 1 | [answer33] 2 | 3 | df.loc[3,'age'] = 40 4 | df.head() -------------------------------------------------------------------------------- /input/a_034.txt: -------------------------------------------------------------------------------- 1 | [answer34] 2 | 3 | df['sex'][df['sex'] == 'male'] = 0 4 | df['sex'][df['sex'] == 'female'] = 1 5 | df.head() 6 | 7 | 8 | [Tips] 9 | df['sex'][df['sex'] == 'male'] = 0 10 | ↑ 11 | 「df['sex']において、dfのsex列がmaleと 12 | なっているレコードを0に置き換える」 13 | 14 | .replace()メソッドを用いて以下のようにしても 15 | 同様の結果になる 16 | 17 | df['sex'] = df['sex'].replace({'male': 0, 'female': 1}) -------------------------------------------------------------------------------- /input/a_035.txt: -------------------------------------------------------------------------------- 1 | [answer35] 2 | 3 | df['fare'] = df['fare'] + 100 4 | df.head() -------------------------------------------------------------------------------- /input/a_036.txt: -------------------------------------------------------------------------------- 1 | [answer36] 2 | 3 | df['fare'] = df['fare'] * 2 4 | df.head() -------------------------------------------------------------------------------- /input/a_037.txt: -------------------------------------------------------------------------------- 1 | [answer37] 2 | 3 | df['fare'] = df['fare'].round() 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・小数点以下を丸めるときは round() を使用する 9 | ・丸めるとは0.5より小さいときは切り捨て、0.5より大きい 10 | 　ときは切り上げること(ちょうど0.5のときは、結果が偶数と 11 | 　なるように切り捨て・切り上げを行う) 12 | ・()に整数nを渡すと、小数点以下n桁に丸める 13 | ・()に-1を指定すると10の位、-2を指定すると100の 14 | 　位に丸められる 15 | 16 | ex) 17 | df['fare'].round(2) 小数点2桁に丸める 18 | 123.456　→　123.46 19 | 20 | df['fare'].round(-2) 整数2桁に丸める 21 | 123.456　→　100.0 -------------------------------------------------------------------------------- /input/a_038.txt: -------------------------------------------------------------------------------- 1 | [answer38] 2 | 3 | df['test'] = 1 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・新規に列を追加するときは上記のように書く -------------------------------------------------------------------------------- /input/a_039.txt: -------------------------------------------------------------------------------- 1 | [answer39] 2 | 3 | df['test'] = df['cabin'].str.cat(df['embarked'],sep='_') 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・列同士の結合にはstr.cat()を使用 9 | ・下記のコードでも結合可能 10 | 11 | df['test'] = df['cabin'] + '_' + df['embarked'] -------------------------------------------------------------------------------- /input/a_040.txt: -------------------------------------------------------------------------------- 1 | [answer40] 2 | 3 | df['test'] = df['age'].astype(str).str.cat(df['embarked'],sep='_') 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・数値変数と文字変数(カテゴリカル変数)は結合できないため、 9 | 　片方の列のデータ型を、もう片方の列のデータ型に変換する 10 | ・データ型の変換には astype を使用する(問題8を参照) 11 | ・ここでは数値変数のageを、文字変数(str)に変換 12 | ・下記のコードでも結合可能 13 | 14 | df['test'] = df['age'].astype(str) + '_' + df['embarked'] -------------------------------------------------------------------------------- /input/a_041.txt: -------------------------------------------------------------------------------- 1 | [answer41] 2 | 3 | df = df.drop('body',axis=1) 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・行・列の削除をするにはdropを使用 9 | ・列を削除する場合は、axis=1を指定 10 | （行を削除する場合は、axis=0） -------------------------------------------------------------------------------- /input/a_042.txt: -------------------------------------------------------------------------------- 1 | [answer42] 2 | 3 | df = df.drop(3,axis=0) 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・行・列の削除をするにはdropを使用 9 | ・行を削除する場合は、axis=0を指定 10 | （列を削除する場合は、axis=1） -------------------------------------------------------------------------------- /input/a_043.txt: -------------------------------------------------------------------------------- 1 | [answer43] 2 | 3 | df2.columns = ['name', 'class', 'Biology', 'Physics', 'Chemistry'] 4 | df2.head() 5 | 6 | 7 | [Tips] 8 | ・データフレーム.columns = リストで 9 | 　列名を一括変更 10 | ・renameを用いて以下のように変更することも可能 11 | 12 | df2 = df2.rename(columns={'English' : 'Biology','Mathematics' : 'Physics', 'History' : 'Chemistry'}) -------------------------------------------------------------------------------- /input/a_044.txt: -------------------------------------------------------------------------------- 1 | [answer44] 2 | 3 | df2 = df2.rename(columns={'English' : 'Biology'}) 4 | df2.head() 5 | 6 | 7 | [Tips] 8 | rename(columns={'English' : 'Biology'})で 9 | 一部の列名のみ変更可能 -------------------------------------------------------------------------------- /input/a_045.txt: -------------------------------------------------------------------------------- 1 | [answer45] 2 | 3 | df2 = df2.rename(index={1 : 10}) 4 | df2.head() 5 | 6 | 7 | [Tips] 8 | rename(index={1 : 10})で 9 | 一部の行名を変更可能 -------------------------------------------------------------------------------- /input/a_046.txt: -------------------------------------------------------------------------------- 1 | [answer46] 2 | 3 | df.isnull().sum() 4 | 5 | 6 | [Tips] 7 | ・isnull().sum()で欠損値数を確認 8 | ・欠損値じゃないレコードの数を確認したい場合は、 9 | 　notnull().sum() -------------------------------------------------------------------------------- /input/a_047.txt: -------------------------------------------------------------------------------- 1 | [answer47] 2 | 3 | df['age'] = df['age'].fillna(30) 4 | df['age'].isnull().sum() 5 | 6 | 7 | [Tips] 8 | 欠損値の補完にはfillnaを使用 -------------------------------------------------------------------------------- /input/a_048.txt: -------------------------------------------------------------------------------- 1 | [answer48] 2 | 3 | df = df.dropna() 4 | df.isnull().sum() 5 | 6 | 7 | [Tips] 8 | 欠損値を含む行の削除には dropna を使用 -------------------------------------------------------------------------------- /input/a_049.txt: -------------------------------------------------------------------------------- 1 | [answer49] 2 | 3 | df['survived'].values 4 | 5 | 6 | [Tips] 7 | ・pandas.DataFrameやpandas.Seriesをndarray形式(配列)に 8 | 　変換するにはvaluesを使用 9 | ・機械学習ライブラリのscikit-learnではndarray形式で入力する 10 | 　必要があるため、そのような際にDataFrameをndarray形式に変換する -------------------------------------------------------------------------------- /input/a_050.txt: -------------------------------------------------------------------------------- 1 | [answer50] 2 | 3 | df.sample(frac=1) 4 | 5 | 6 | [Tips] 7 | 行をシャッフルして表示する場合は、 8 | sample(frac=1)を使用 -------------------------------------------------------------------------------- /input/a_051.txt: -------------------------------------------------------------------------------- 1 | [answer51] 2 | 3 | df.sample(frac=1).reset_index() 4 | 5 | 6 | [Tips] 7 | インデックスを振り直すときはreset_indexを使用 -------------------------------------------------------------------------------- /input/a_052.txt: -------------------------------------------------------------------------------- 1 | [answer52] 2 | 3 | print(df2.duplicated().value_counts()) 4 | df2 = df2.drop_duplicates() 5 | df2 6 | 7 | 8 | [Tips] 9 | ・重複行数をカウントする時は duplicated().value_counts() 10 | ・重複行を削除する時は drop_duplicates() -------------------------------------------------------------------------------- /input/a_053.txt: -------------------------------------------------------------------------------- 1 | [answer53] 2 | 3 | df['name'].str.upper() 4 | 5 | 6 | [Tips] 7 | str.upper()で小文字を大文字に変換 -------------------------------------------------------------------------------- /input/a_054.txt: -------------------------------------------------------------------------------- 1 | [answer54] 2 | 3 | df['name'].str.lower() 4 | 5 | 6 | [Tips] 7 | str.lower()で大文字を小文字に変換 -------------------------------------------------------------------------------- /input/a_055.txt: -------------------------------------------------------------------------------- 1 | [answer55] 2 | 3 | df['sex'] = df['sex'].replace('female','Python') 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・数値、文字列の置換にはreplace()を使用 9 | ・replace(a,b)でaをbに置換 10 | ・数値でなく文字列の場合は replace('a','b')とする -------------------------------------------------------------------------------- /input/a_056.txt: -------------------------------------------------------------------------------- 1 | [answer56] 2 | 3 | import re 4 | df['name'][0] = re.sub('Elisabeth','',df['name'][0]) 5 | df['name'][0] 6 | 7 | 8 | [Tips] 9 | ・部分一致の文字列消去にはre.sub()を使用 10 | ・re.sub('消したい文字列','','元々の文字列') のように使う 11 | ・完全一致で文字列を消去するときはreplaceを使用可能 12 | 13 | ex) 14 | df['sex'] = df['sex'].repalce('female','') -------------------------------------------------------------------------------- /input/a_057.txt: -------------------------------------------------------------------------------- 1 | [answer57] 2 | 3 | df5['test2'] = df5['都道府県'].str.rstrip() +'_'+ df5['市区町村'] 4 | df5.head() 5 | 6 | 7 | [Tips] 8 | ・文字列右側の空白を削除　str.rstrip() 9 | ・文字列の両端の空白を削除　str.strip() 10 | ・文字列の左側の空白を削除　str.lstrip() -------------------------------------------------------------------------------- /input/a_058.txt: -------------------------------------------------------------------------------- 1 | [answer58] 2 | 3 | df2 = df2.transpose() 4 | df2 5 | 6 | 7 | [Tips] 8 | ・データフレームの行と列を入れ替えるときはtranspose()を使用 9 | ・df2.Tとすることでも行と列を入れ替え可能 -------------------------------------------------------------------------------- /input/a_059.txt: -------------------------------------------------------------------------------- 1 | [answer59] 2 | 3 | df2 = pd.merge(df2,df3,on='name',how='left') 4 | df2 5 | 6 | 7 | [Tips] 8 | ・左結合では、df2に存在するレコードにdf3のレコードを結合する 9 | ・on=''で結合キーを指定 10 | ・how=''で結合方法を指定 -------------------------------------------------------------------------------- /input/a_060.txt: -------------------------------------------------------------------------------- 1 | [answer60] 2 | 3 | df2 = pd.merge(df2,df3,on='name',how='right') 4 | df2 5 | 6 | 7 | [Tips] 8 | ・右結合では、df3に存在するレコードにdf2のレコードを結合する 9 | ・on=''で結合キーを指定 10 | ・how=''で結合方法を指定 -------------------------------------------------------------------------------- /input/a_061.txt: -------------------------------------------------------------------------------- 1 | [answer61] 2 | 3 | df2 = pd.merge(df2,df3,on='name',how='inner') 4 | df2 5 | 6 | 7 | [Tips] 8 | ・内部結合では、df2とdf3の共通のキーのみで結合する 9 | ・on=''で結合キーを指定 10 | ・how=''で結合方法を指定 -------------------------------------------------------------------------------- /input/a_062.txt: -------------------------------------------------------------------------------- 1 | [answer62] 2 | 3 | df2 = pd.merge(df2,df3,on='name',how='outer') 4 | df2 5 | 6 | 7 | [Tips] 8 | ・外部結合では、df2とdf3の両方に存在するレコードが 9 | 　残るように結合する 10 | ・on=''で結合キーを指定 11 | ・how=''で結合方法を指定 -------------------------------------------------------------------------------- /input/a_063.txt: -------------------------------------------------------------------------------- 1 | [answer63] 2 | 3 | df2 = pd.concat([df2,df4],axis=1) 4 | df2 5 | 6 | 7 | [Tips] 8 | ・複数のデータフレームを連結するときはpd.concatを使用 9 | ・axis=0で行方向、axis=1で列方向に連結 10 | ・pd.concat([df2,df4],axis=1)でdf2とdf4を列方向に連結 -------------------------------------------------------------------------------- /input/a_064.txt: -------------------------------------------------------------------------------- 1 | [answer64] 2 | 3 | df2 = pd.concat([df2,df4],axis=1) 4 | df2 = df2.loc[:,~df2.columns.duplicated()] 5 | df2 6 | 7 | 8 | [Tips] 9 | df2.loc[:,~df2.columns.duplicated()]により 10 | 重複した列を消去 -------------------------------------------------------------------------------- /input/a_065.txt: -------------------------------------------------------------------------------- 1 | [answer65] 2 | 3 | df2 = pd.concat([df2,df4],axis=0) 4 | df2 5 | 6 | 7 | [Tips] 8 | ・複数のデータフレームを連結するときはpd.concatを使用 9 | ・axis=0で行方向、axis=1で列方向に連結 10 | ・pd.concat([df2,df4],axis=0)でdf2とdf4を行方向に連結 -------------------------------------------------------------------------------- /input/a_066.txt: -------------------------------------------------------------------------------- 1 | [answer66] 2 | 3 | df['age'].mean() 4 | 5 | 6 | [Tips] 7 | ・列の平均値はmean()で確認 -------------------------------------------------------------------------------- /input/a_067.txt: -------------------------------------------------------------------------------- 1 | [answer67] 2 | 3 | df['age'].median() 4 | 5 | 6 | [Tips] 7 | ・列の中央値はmedian()で確認 -------------------------------------------------------------------------------- /input/a_068.txt: -------------------------------------------------------------------------------- 1 | [answer68] 2 | 3 | df2 = df2.drop(['class'],axis=1) 4 | print(df2.sum(axis=1)) #行方向の合計 5 | print(df2.sum()) #列方向の合計 6 | 7 | 8 | [Tips] 9 | ・合計値の確認はsum()を使用 10 | ・引数空欄の場合、デフォルトは列方向の合計 11 | ・引数にaxis=1を指定すると行方向の合計 -------------------------------------------------------------------------------- /input/a_069.txt: -------------------------------------------------------------------------------- 1 | [answer69] 2 | 3 | df2['English'].max() 4 | 5 | 6 | [Tips] 7 | 最大値の確認はmax()を使用 -------------------------------------------------------------------------------- /input/a_070.txt: -------------------------------------------------------------------------------- 1 | [answer70] 2 | 3 | df2['English'].min() 4 | 5 | 6 | [Tips] 7 | 最小値の確認はmin()を使用 -------------------------------------------------------------------------------- /input/a_071.txt: -------------------------------------------------------------------------------- 1 | [answer71] 2 | 3 | df2 =df2.drop('name',axis=1) 4 | print(df2.groupby('class').max()) 5 | print(df2.groupby('class').min()) 6 | print(df2.groupby('class').mean()) 7 | 8 | 9 | [Tips] 10 | 指定の列名でグルーピングしたい場合は 11 | groupby('列名')を使用する -------------------------------------------------------------------------------- /input/a_072.txt: -------------------------------------------------------------------------------- 1 | [answer72] 2 | 3 | df.describe() 4 | 5 | 6 | [Tips] 7 | データフレームの基本統計量を確認したい場合は 8 | describe()を使用 -------------------------------------------------------------------------------- /input/a_073.txt: -------------------------------------------------------------------------------- 1 | [answer73] 2 | 3 | df.corr() 4 | 5 | 6 | [Tips] 7 | データフレームの列間の相関係数を確認したい場合は 8 | corr()を使用 -------------------------------------------------------------------------------- /input/a_074.txt: -------------------------------------------------------------------------------- 1 | [answer74] 2 | 3 | from sklearn.preprocessing import StandardScaler 4 | 5 | df2 = df2.drop(['name','class'],axis=1) #不要列の削除 6 | 7 | #標準化を定義 8 | scaler = StandardScaler() 9 | scaler.fit(df2) 10 | 11 | #変換とデータフレームへの置換 12 | scaler.transform(df2) # 変換のみ 13 | df2_std = pd.DataFrame(scaler.transform(df2), columns=df2.columns) # 変換とデータフレームへの置換をまとめて行うとこうなる 14 | 15 | df2_std.describe() #stdが等しくなっていることを確認 16 | 17 | 18 | [Tips] 19 | データフレームを標準化する場合は、scikit-learnの 20 | StandardScalerを使用 -------------------------------------------------------------------------------- /input/a_075.txt: -------------------------------------------------------------------------------- 1 | [answer75] 2 | 3 | from sklearn.preprocessing import StandardScaler 4 | 5 | #標準化を定義 6 | scaler = StandardScaler() 7 | scaler.fit(df2['English'].values.reshape(-1,1)) 8 | 9 | #変換とデータフレームへの置換 10 | scaler.transform(df2['English'].values.reshape(-1,1)) # 変換のみ 11 | df2_std = pd.DataFrame(scaler.transform(df2['English'].values.reshape(-1,1))) # 変換とデータフレームへの置換をまとめて行うとこうなる 12 | 13 | df2_std.describe() #stdが【74】のEnglishと等しくなっていることを確認 14 | 15 | 16 | [Tips] 17 | ・データフレームのひとつの列を標準化する場合は、 18 | 　values.reshape(-1,1)で配列変換してやる方法もある 19 | ・reshape(-1,1)でn行1列に変換 -------------------------------------------------------------------------------- /input/a_076.txt: -------------------------------------------------------------------------------- 1 | [answer76] 2 | 3 | from sklearn.preprocessing import MinMaxScaler 4 | 5 | df2 = df2.drop(['name','class'],axis=1) #不要列の削除 6 | 7 | # Min-Maxスケーリングを定義 8 | scaler = MinMaxScaler() 9 | scaler.fit(df2) 10 | 11 | # 変換とデータフレームへの置換 12 | scaler.transform(df2) # 変換のみ 13 | df2_std = pd.DataFrame(scaler.transform(df2), columns=df2.columns) # 変換とデータフレームへの置換をまとめて行うとこうなる 14 | 15 | df2_std.describe() #minが0、maxが1になっていることを確認 16 | 17 | 18 | [Tips] 19 | ・データフレームをMin-Maxスケーリングする場合は、scikit-learnの 20 | 　StandardScalerを使用 21 | ・Min-Maxスケーリングでは最小値が0、最大値が1となるように 22 | 　データを変換する -------------------------------------------------------------------------------- /input/a_077.txt: -------------------------------------------------------------------------------- 1 | [answer77] 2 | 3 | print(df['fare'].idxmax()) 4 | print(df['fare'].idxmin()) 5 | 6 | [Tips] 7 | データフレームの最大値、最小値の行名を 8 | 求める場合はidxmax、idxminを使用 -------------------------------------------------------------------------------- /input/a_078.txt: -------------------------------------------------------------------------------- 1 | [answer78] 2 | 3 | print(df['fare'].quantile([0, 0.25, 0.5, 0.75, 1.0])) 4 | 5 | 6 | [Tips] 7 | ・パーセンタイルを取得する場合は quantile()を使用 8 | ・50パーセンタイル=中央値、0パーセンタイル=最小値、 9 | 　100パーセンタイル=最大値 -------------------------------------------------------------------------------- /input/a_079.txt: -------------------------------------------------------------------------------- 1 | [answer79] 2 | 3 | print(df['age'].mode()) 4 | print(df['age'].value_counts()) 5 | 6 | 7 | [Tips] 8 | 最頻値を取得する場合は mode()を使用 -------------------------------------------------------------------------------- /input/a_080.txt: -------------------------------------------------------------------------------- 1 | [answer80] 2 | 3 | from sklearn.preprocessing import LabelEncoder 4 | 5 | le = LabelEncoder() #ラベルエンコーダのインスタンスを作成 6 | 7 | df['sex'] = le.fit_transform(df['sex']) #エンコーディング 8 | df.head() 9 | 10 | 11 | [Tips] 12 | ・機械学習では文字列をそのまま、学習アルゴリズムに 13 | 　入力できないため、数値に変換する。LabelEncoder()では 14 | 　例えば、以下のように文字列を数値に変換する。 15 | 16 | 　male → 0 17 | 　female → 1 18 | 19 | ・RandomForestなど決定木での分類問題を 20 | 　解く場合には、ラベルエンコーディングする 21 | 　ことが多い -------------------------------------------------------------------------------- /input/a_081.txt: -------------------------------------------------------------------------------- 1 | [answer81] 2 | 3 | df = pd.get_dummies(df, columns=['sex']) 4 | df.head() 5 | 6 | 7 | [Tips] 8 | ・機械学習では文字列をそのまま、学習アルゴリズムに 9 | 　入力できないため、数値に変換する。pd.get_dummiesでは 10 | 　One-Hotエンコーディングが可能 11 | ・回帰問題を解く場合には、One-hotエンコーディングする 12 | 　ことが多い -------------------------------------------------------------------------------- /input/a_082.txt: -------------------------------------------------------------------------------- 1 | [answer82] 2 | 3 | df.hist(figsize=(20,20), color='b') 4 | 5 | 6 | [Tips] 7 | ・データフレームの数値列をヒストグラムで 8 | 　描画したい場合は hist()を使用 9 | ・figsize=()でグラフのサイズを指定可能 10 | ・color=''でグラフの色を指定可能 11 | （'r'にすれば赤色表示） -------------------------------------------------------------------------------- /input/a_083.txt: -------------------------------------------------------------------------------- 1 | [answer83] 2 | 3 | df['age'].plot(kind='hist') 4 | 5 | 6 | [Tips] 7 | ヒストグラムを描画する場合は plot(kind='hist')を使用 -------------------------------------------------------------------------------- /input/a_084.txt: -------------------------------------------------------------------------------- 1 | [answer84] 2 | 3 | df2['sum'] = df2.iloc[:,2:5].sum(axis=1) #3科目合計の列を作成 4 | df2[['name','sum']].plot(kind='bar',x=df2.columns[0]) 5 | 6 | 7 | [Tips] 8 | ・棒グラフを描画する場合は plot(kind='bar')を使用 9 | ・df2.columns[0]はname列のこと。x=df2.columns[0]を指定し 10 | 　x軸をname列にする(指定しないとどうなるかは試してみて下さい) -------------------------------------------------------------------------------- /input/a_085.txt: -------------------------------------------------------------------------------- 1 | [answer85] 2 | 3 | df2[['name','English','Mathematics','History']].plot(kind='bar',figsize=(10,4),x=df2.columns[0]) 4 | 5 | 6 | [Tips] 7 | ・棒グラフを描画する場合は plot(kind='bar')を使用 8 | ・「df2[['name','English','Mathematics','History']]」のように 9 | 　使用したい列のみに絞る 10 | ・df2.columns[0]はname列のこと。x=df2.columns[0]を指定し 11 | 　x軸をname列にする(指定しないとどうなるかは試してみて下さい) -------------------------------------------------------------------------------- /input/a_086.txt: -------------------------------------------------------------------------------- 1 | [answer86] 2 | 3 | df2[['name','English','Mathematics','History']].plot(kind='bar',figsize=(10,4), 4 | x=df2.columns[0],stacked=True) 5 | 6 | 7 | [Tips] 8 | ・棒グラフを積み上げ表示する場合は stacked=Trueを指定 9 | ・df2.columns[0]はname列のこと。x=df2.columns[0]を指定し 10 | 　x軸をname列にする(指定しないとどうなるかは試してみて下さい) -------------------------------------------------------------------------------- /input/a_087.txt: -------------------------------------------------------------------------------- 1 | [answer87] 2 | 3 | %matplotlib inline 4 | from pandas.plotting import scatter_matrix 5 | 6 | _ = scatter_matrix(df,figsize=(20,20)) 7 | 8 | 9 | [Tips] 10 | ・%matplotlib inlineを記述することでJupyter Notebook上に 11 | 　インラインで表示 12 | ・データフレームの各列間の散布図を描画するには 13 | 　scatter_matrixを使用 14 | ・対角線はヒストグラム -------------------------------------------------------------------------------- /input/a_088.txt: -------------------------------------------------------------------------------- 1 | [answer88] 2 | 3 | df.plot(kind='scatter',x='age',y='fare',figsize=(8,6)) 4 | 5 | 6 | [Tips] 7 | ・散布図を描画するには plot(kind='scatter')を使用 8 | ・figsizeでグラフサイズを指定可能 -------------------------------------------------------------------------------- /input/a_089.txt: -------------------------------------------------------------------------------- 1 | [answer89] 2 | 3 | df.plot(kind='scatter',x='age',y='fare',figsize=(8,6),title='age-fare scatter') 4 | 5 | 6 | [Tips] 7 | ・散布図を描画するには plot(kind='scatter')を使用 8 | ・figsizeでグラフサイズを指定可能 9 | ・title=''でグラフタイトルを表示可能 -------------------------------------------------------------------------------- /input/a_090.txt: -------------------------------------------------------------------------------- 1 | [answer90] 2 | 3 | from sklearn.preprocessing import LabelEncoder 4 | 5 | le = LabelEncoder() #ラベルエンコーダのインスタンスを作成 6 | 7 | df_copy['sex'] = le.fit_transform(df_copy['sex']) #エンコーディング 8 | df_copy['embarked'] = le.fit_transform(df_copy['embarked'].astype(str)) #ここ、なぜかstrに変換しないとエラー発生 9 | df_copy.head() 10 | 11 | 12 | [Tips] 13 | ・機械学習では文字列をそのまま、学習アルゴリズムに 14 | 　入力できないため、数値に変換する。LabelEncoder()では 15 | 　例えば、以下のように文字列を数値に変換する。 16 | 17 | 　male → 0 18 | 　female → 1 19 | 20 | ・RandomForestなど決定木での分類問題を 21 | 　解く場合には、ラベルエンコーディングする 22 | 　ことが多い -------------------------------------------------------------------------------- /input/a_091.txt: -------------------------------------------------------------------------------- 1 | [answer91] 2 | 3 | df_copy.isnull().sum() 4 | 5 | 6 | [Tips] 7 | ・isnull().sum()で欠損値数を確認 8 | ・欠損値じゃないレコードの数を確認したい場合は、 9 | 　notnull().sum() -------------------------------------------------------------------------------- /input/a_092.txt: -------------------------------------------------------------------------------- 1 | [answer92] 2 | 3 | df_copy['age'] = df_copy['age'].fillna(df_copy['age'].mean()) #欠損値にageの平均値で補完 4 | df_copy['fare'] = df_copy['fare'].fillna(df_copy['fare'].mean()) #欠損値にfareの平均値で補完 5 | print(df_copy.isnull().sum()) 6 | 7 | 8 | [Tips] 9 | 欠損値の補完にはfillnaを使用 -------------------------------------------------------------------------------- /input/a_093.txt: -------------------------------------------------------------------------------- 1 | [answer93] 2 | 3 | df_copy = df_copy.drop(['name', 'ticket', 'cabin', 'boat', 'body', 'home.dest'],axis=1) 4 | df_copy 5 | 6 | 7 | [Tips] 8 | ・行・列の削除をするにはdropを使用 9 | ・列を削除する場合は、axis=1を指定 10 | （行を削除する場合は、axis=0） -------------------------------------------------------------------------------- /input/a_094.txt: -------------------------------------------------------------------------------- 1 | [answer94] 2 | 3 | features = df_copy[['pclass','age','sex','fare','embarked']].values 4 | target = df_copy['survived'].values 5 | 6 | 7 | [Tips] 8 | ・pandas.DataFrameやpandas.Seriesをndarray形式(配列)に 9 | 　変換するにはvaluesを使用 10 | ・機械学習ライブラリのscikit-learnではndarray形式で入力する 11 | 　必要があるため、そのような際にDataFrameをndarray形式に変換する -------------------------------------------------------------------------------- /input/a_095.txt: -------------------------------------------------------------------------------- 1 | [answer95] 2 | 3 | from sklearn.model_selection import train_test_split 4 | 5 | (features , test_X , target , test_y) = train_test_split(features, target , test_size = 0.3 , random_state = 0) 6 | 7 | 8 | [Tips] 9 | ・データを学習データ、テストデータに分割する場合は train_test_splitを使用 10 | ・test_sizeで分割する割合を指定 11 | ・random_stateでシード値を指定することでデータ分割時の乱数を固定 12 | （検証で乱数を固定したい時に使用） -------------------------------------------------------------------------------- /input/a_096.txt: -------------------------------------------------------------------------------- 1 | [answer96] 2 | 3 | from sklearn.ensemble import RandomForestClassifier 4 | 5 | model = RandomForestClassifier(n_estimators=100,random_state=0) # ランダムフォレストのインスタンスを作成 6 | 7 | model.fit(features,target) # 学習の実行 8 | 9 | 10 | [Tips] 11 | ・RandomForestClassifierでランダムフォレストを呼び出し 12 | ・ハイパーパラメータはn_estimatorsやmax_depthなどがある 13 | ・.fit()にて学習を実行 14 | ・入力値はndarray形式でないといけない 15 | （そのため、【94】にてndaaray形式に変換を実施） -------------------------------------------------------------------------------- /input/a_097.txt: -------------------------------------------------------------------------------- 1 | [answer97] 2 | 3 | pred = model.predict(test_X) 4 | 5 | 6 | [Tips] 7 | .predict()にて予測を実行 -------------------------------------------------------------------------------- /input/a_098.txt: -------------------------------------------------------------------------------- 1 | [answer98] 2 | 3 | from sklearn.metrics import accuracy_score 4 | 5 | accuracy_score(pred,test_y) 6 | 7 | 8 | [Tips] 9 | ・accuracy_score(正解率)にて予測精度を検証 10 | ・予測精度の評価指標には様々あるため、タスクに 11 | 　合わせて適切な指標を選択 12 | 13 | （参考）分類タスクの評価指標 14 | 　https://qiita.com/jyori112/items/110596b4f04e4e1a3c9b -------------------------------------------------------------------------------- /input/a_099.txt: -------------------------------------------------------------------------------- 1 | [answer99] 2 | 3 | importance = model.feature_importances_ 4 | 5 | print('Feature Importances:') 6 | for i, feat in enumerate(['pclass','age','sex','fare','embarked']): 7 | print('\t{0:20s} : {1:>.5f}'.format(feat, importance[i])) 8 | 9 | 10 | [Tips] 11 | .feature_importances_にてランダムフォレストの 12 | 学習における各列(特徴量)の重要度を確認可能 -------------------------------------------------------------------------------- /input/a_100.txt: -------------------------------------------------------------------------------- 1 | [answer100] 2 | 3 | df_pred = pd.DataFrame(pred) 4 | df_pred.to_csv('../output/submission.csv',header=None) 5 | 6 | 7 | [Tips] 8 | ・to_csvでcsv形式で出力 9 | ・行番号、列名を削除して出力したいときは 10 | 　index=None,header=Noneをつける -------------------------------------------------------------------------------- /input/data1.csv: -------------------------------------------------------------------------------- 1 | name,class,English,Mathematics,History 2 | A,1,80,55,65 3 | B,1,55,100,83 4 | C,2,65,70,95 5 | D,1,40,63,70 6 | E,2,76,82,79 7 | F,2,93,68,81 8 | G,1,100,85,52 9 | H,2,73,52,96 10 | B,1,55,100,83 11 | D,1,40,63,70 12 | -------------------------------------------------------------------------------- /input/data1_2.csv: -------------------------------------------------------------------------------- 1 | name,Biology,Physics 2 | A,58,100 3 | C,79,60 4 | D,95,89 5 | F,45,69 6 | I,63,91 7 | J,87,76 8 | K,73,83 9 | -------------------------------------------------------------------------------- /input/data1_3.csv: -------------------------------------------------------------------------------- 1 | name,Biology,Physics 2 | A,58,100 3 | B,80,63 4 | C,79,60 5 | D,95,89 6 | E,68,55 7 | F,45,69 8 | G,52,68 9 | H,89,70 10 | B,61,64 11 | D,95,89 12 | -------------------------------------------------------------------------------- /input/data2.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kunishou/Pandas_100_knocks/1df5bb9545f06931c255e079cbe9e236baa5058a/input/data2.csv -------------------------------------------------------------------------------- /input/q_000.txt: -------------------------------------------------------------------------------- 1 | [question0] 2 | 3 | こんな感じで問題文が表示されます 4 | 5 | ------------------------------------------- 6 | -------------------------------------------------------------------------------- /input/q_001.txt: -------------------------------------------------------------------------------- 1 | [question1] 2 | 3 | # dfに読み込んだデータの最初の5行を表示 4 | -------------------------------------------------------------------------------- /input/q_002.txt: -------------------------------------------------------------------------------- 1 | [question2] 2 | 3 | # dfに読み込んだデータの最後の5行を表示 4 | -------------------------------------------------------------------------------- /input/q_003.txt: -------------------------------------------------------------------------------- 1 | [question3] 2 | 3 | # dfのDataFrameサイズを確認 4 | -------------------------------------------------------------------------------- /input/q_004.txt: -------------------------------------------------------------------------------- 1 | [question4] 2 | 3 | # inputフォルダ内のdata1.csvファイルを 4 | # 読み込みdf2に格納して、最初の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_005.txt: -------------------------------------------------------------------------------- 1 | [question5] 2 | 3 | # dfのfareの列で昇順に並び替えて表示 4 | -------------------------------------------------------------------------------- /input/q_006.txt: -------------------------------------------------------------------------------- 1 | [question6] 2 | 3 | # df_copyにdfをコピーして、最初の5行を表示 4 | -------------------------------------------------------------------------------- /input/q_007.txt: -------------------------------------------------------------------------------- 1 | [question7] 2 | 3 | # ① dfの各列のデータ型を確認 4 | # ② dfのcabinの列のデータ型を確認 5 | -------------------------------------------------------------------------------- /input/q_008.txt: -------------------------------------------------------------------------------- 1 | [question8] 2 | 3 | # ① dfのpclassの列のデータ型をdtypeで確認 4 | # ② 数値型から文字型に変換し、データ型をdtypeで確認 5 | -------------------------------------------------------------------------------- /input/q_009.txt: -------------------------------------------------------------------------------- 1 | [question9] 2 | 3 | # dfのレコード数(行数)を確認 4 | -------------------------------------------------------------------------------- /input/q_010.txt: -------------------------------------------------------------------------------- 1 | [question10] 2 | 3 | # dfのレコード数(行数)、各列のデータ型、欠損値の有無を確認 4 | -------------------------------------------------------------------------------- /input/q_011.txt: -------------------------------------------------------------------------------- 1 | [question11] 2 | 3 | # dfのsex,cabinの列の要素を確認 4 | -------------------------------------------------------------------------------- /input/q_012.txt: -------------------------------------------------------------------------------- 1 | [question12] 2 | 3 | # dfの列名一覧をlist形式で表示 4 | -------------------------------------------------------------------------------- /input/q_013.txt: -------------------------------------------------------------------------------- 1 | [question13] 2 | 3 | # dfのインデックス一覧をndaaray形式で表示 4 | -------------------------------------------------------------------------------- /input/q_014.txt: -------------------------------------------------------------------------------- 1 | [question14] 2 | 3 | # dfのnameの列のみ表示 4 | -------------------------------------------------------------------------------- /input/q_015.txt: -------------------------------------------------------------------------------- 1 | [question15] 2 | 3 | # dfのnameとsexの列のみ表示 4 | -------------------------------------------------------------------------------- /input/q_016.txt: -------------------------------------------------------------------------------- 1 | [question16] 2 | 3 | # dfのindex(行)の4行目までを表示 4 | -------------------------------------------------------------------------------- /input/q_017.txt: -------------------------------------------------------------------------------- 1 | [question17] 2 | 3 | # dfのindex(行)の4行目から10行目までを表示 4 | -------------------------------------------------------------------------------- /input/q_018.txt: -------------------------------------------------------------------------------- 1 | [question18] 2 | 3 | # locを使ってdf全体を表示 4 | -------------------------------------------------------------------------------- /input/q_019.txt: -------------------------------------------------------------------------------- 1 | [question19] 2 | 3 | # locを使ってdfのfare列をすべて表示 4 | -------------------------------------------------------------------------------- /input/q_020.txt: -------------------------------------------------------------------------------- 1 | [question20] 2 | 3 | # locを使ってdfのfare列の10のラベルまで表示 4 | -------------------------------------------------------------------------------- /input/q_021.txt: -------------------------------------------------------------------------------- 1 | [question21] 2 | 3 | # locを使ってdfのnameとticketの列をすべて表示 4 | -------------------------------------------------------------------------------- /input/q_022.txt: -------------------------------------------------------------------------------- 1 | [question22] 2 | 3 | # locを使ってdfのnameからcabinまでの列をすべて表示 4 | -------------------------------------------------------------------------------- /input/q_023.txt: -------------------------------------------------------------------------------- 1 | [question22] 2 | 3 | # ilocを使ってdfのage列を5行目まで表示 4 | -------------------------------------------------------------------------------- /input/q_024.txt: -------------------------------------------------------------------------------- 1 | [question24] 2 | 3 | # dfのname,age,sexの列のみ抽出しdf_copyに格納 4 | # その後outputフォルダにcsvファイルで出力 5 | # 出力ファイル名はsmaple.csv 6 | -------------------------------------------------------------------------------- /input/q_025.txt: -------------------------------------------------------------------------------- 1 | [question25] 2 | 3 | # dfのage列の値が30以上のデータのみ抽出 4 | -------------------------------------------------------------------------------- /input/q_026.txt: -------------------------------------------------------------------------------- 1 | [question26] 2 | 3 | # dfのsex列がfemaleのデータのみ抽出 4 | -------------------------------------------------------------------------------- /input/q_027.txt: -------------------------------------------------------------------------------- 1 | [question27] 2 | 3 | # dfのsex列がfemaleでかつageが40以上のデータのみ抽出 4 | -------------------------------------------------------------------------------- /input/q_028.txt: -------------------------------------------------------------------------------- 1 | [question28] 2 | 3 | # queryを用いてdfのsex列がfemaleでかつageが40以上のデータのみ抽出 4 | -------------------------------------------------------------------------------- /input/q_029.txt: -------------------------------------------------------------------------------- 1 | [question29] 2 | 3 | # dfのname列に文字列「Mrs」が含まれるデータを表示 4 | -------------------------------------------------------------------------------- /input/q_030.txt: -------------------------------------------------------------------------------- 1 | [question30] 2 | 3 | # dfの中で文字型の列のみを表示 4 | -------------------------------------------------------------------------------- /input/q_031.txt: -------------------------------------------------------------------------------- 1 | [question31] 2 | 3 | # dfの各列の要素数の確認 4 | -------------------------------------------------------------------------------- /input/q_032.txt: -------------------------------------------------------------------------------- 1 | [question32] 2 | 3 | # dfのembarked列の要素と出現回数の確認 4 | -------------------------------------------------------------------------------- /input/q_033.txt: -------------------------------------------------------------------------------- 1 | [question33] 2 | 3 | # dfのindex名が「3」のage列を 4 | # 30から40に変更し、先頭の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_034.txt: -------------------------------------------------------------------------------- 1 | [question34] 2 | 3 | # dfのsex列にてmale→0、femlae→1に 4 | # 変更し、先頭の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_035.txt: -------------------------------------------------------------------------------- 1 | [question35] 2 | 3 | # dfのfare列に100を足して、 4 | # 先頭の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_036.txt: -------------------------------------------------------------------------------- 1 | [question36] 2 | 3 | # dfのfare列を2を掛けて、 4 | # 先頭の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_037.txt: -------------------------------------------------------------------------------- 1 | [question37] 2 | 3 | # dfのfare列を小数点以下で丸める 4 | #print(ans[37]) #回答表示 5 | -------------------------------------------------------------------------------- /input/q_038.txt: -------------------------------------------------------------------------------- 1 | [question38] 2 | 3 | # dfに列名「test」で値がすべて1の 4 | # カラムを追加し、先頭の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_039.txt: -------------------------------------------------------------------------------- 1 | [question39] 2 | 3 | # dfにcabinとembarkedの列を「_」で 4 | # 結合した列を追加(列名は「test」)し、 5 | # 先頭の5行を表示 6 | -------------------------------------------------------------------------------- /input/q_040.txt: -------------------------------------------------------------------------------- 1 | [question40] 2 | 3 | # dfにageとembarkedの列を「_」で 4 | # 結合した列を追加(列名は「test」)し、 5 | # 先頭の5行を表示 6 | -------------------------------------------------------------------------------- /input/q_041.txt: -------------------------------------------------------------------------------- 1 | [question41] 2 | 3 | # dfからbodyの列を削除し、最初の5行を表示 4 | -------------------------------------------------------------------------------- /input/q_042.txt: -------------------------------------------------------------------------------- 1 | [question42] 2 | 3 | # dfからインデックス名「3」の行を削除し、最初の5行を表示 4 | -------------------------------------------------------------------------------- /input/q_043.txt: -------------------------------------------------------------------------------- 1 | [question43] 2 | 3 | # df2の列名を'name', 'class', 'Biology', 'Physics', 'Chemistry'に変更 4 | # df2の最初の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_044.txt: -------------------------------------------------------------------------------- 1 | [question44] 2 | 3 | # df2の列名を'English'をBiology'に変更 4 | # df2の最初の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_045.txt: -------------------------------------------------------------------------------- 1 | [question45] 2 | 3 | # df2のインデックス名「1」を「10」に変更 4 | # df2の最初の5行を表示 5 | -------------------------------------------------------------------------------- /input/q_046.txt: -------------------------------------------------------------------------------- 1 | [question46] 2 | 3 | # dfのすべての列の欠損値数を確認 4 | -------------------------------------------------------------------------------- /input/q_047.txt: -------------------------------------------------------------------------------- 1 | [question47] 2 | 3 | # dfのage列の欠損値に30を代入 4 | # その後、ageの欠損値数を確認 5 | -------------------------------------------------------------------------------- /input/q_048.txt: -------------------------------------------------------------------------------- 1 | [question48] 2 | 3 | # dfでひとつでも欠損値がある行を削除 4 | # その後、dfの欠損値数を確認 5 | -------------------------------------------------------------------------------- /input/q_049.txt: -------------------------------------------------------------------------------- 1 | [question49] 2 | 3 | # dfのsurvivedの列をndarray形式(配列)で表示 4 | -------------------------------------------------------------------------------- /input/q_050.txt: -------------------------------------------------------------------------------- 1 | [question50] 2 | 3 | # dfの行をシャッフルして表示 4 | -------------------------------------------------------------------------------- /input/q_051.txt: -------------------------------------------------------------------------------- 1 | [question51] 2 | 3 | # dfの行をシャッフルし、インデックスを振り直して表示 4 | -------------------------------------------------------------------------------- /input/q_052.txt: -------------------------------------------------------------------------------- 1 | [question52] 2 | 3 | # ①df2の重複行数をカウント 4 | # ②df2の重複行を削除し、df2を表示 5 | -------------------------------------------------------------------------------- /input/q_053.txt: -------------------------------------------------------------------------------- 1 | [question53] 2 | 3 | # dfのnameの列をすべて大文字に変換し表示 4 | -------------------------------------------------------------------------------- /input/q_054.txt: -------------------------------------------------------------------------------- 1 | [question54] 2 | 3 | # dfのnameの列をすべて小文字に変換し表示 4 | -------------------------------------------------------------------------------- /input/q_055.txt: -------------------------------------------------------------------------------- 1 | [question55] 2 | 3 | # dfのsex列に含まれる「female」という単語を 4 | # 「Python」に置換。その後、1行目の 5 | # 「female」が「Python」に置き換わったことを確認 6 | -------------------------------------------------------------------------------- /input/q_056.txt: -------------------------------------------------------------------------------- 1 | [question56] 2 | 3 | # dfのname列1行目の「Allen, Miss. Elisabeth Walton」の 4 | # 「Elisabeth」を消去(import reをインポート) 5 | -------------------------------------------------------------------------------- /input/q_057.txt: -------------------------------------------------------------------------------- 1 | [question57] 2 | 3 | # df5の都道府県列と市区町村列を空白がないように 4 | # 「_」で結合(新規列名は「test2」)し、先頭5行を表示 5 | # ※df5の「test」列は通常通り結合した場合の結果 6 | -------------------------------------------------------------------------------- /input/q_058.txt: -------------------------------------------------------------------------------- 1 | [question58] 2 | 3 | # df2の行と列を入れ替えて表示 4 | -------------------------------------------------------------------------------- /input/q_059.txt: -------------------------------------------------------------------------------- 1 | [question59] 2 | 3 | # df2にdf3を左結合（結合キーはname）し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_060.txt: -------------------------------------------------------------------------------- 1 | [question60] 2 | 3 | # df2にdf3を右結合し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_061.txt: -------------------------------------------------------------------------------- 1 | [question61] 2 | 3 | # df2にdf3を内部結合し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_062.txt: -------------------------------------------------------------------------------- 1 | [question62] 2 | 3 | # df2にdf3を外部結合し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_063.txt: -------------------------------------------------------------------------------- 1 | [question63] 2 | 3 | # df2とdf4を列方向に連結し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_064.txt: -------------------------------------------------------------------------------- 1 | [question64] 2 | 3 | # df2とdf4を列方向に連結後、重複している 4 | # name列の片方を削除し、df2に格納 5 | -------------------------------------------------------------------------------- /input/q_065.txt: -------------------------------------------------------------------------------- 1 | [question65] 2 | 3 | # df2とdf2を行方向に連結し、df2に格納 4 | -------------------------------------------------------------------------------- /input/q_066.txt: -------------------------------------------------------------------------------- 1 | [question66] 2 | 3 | # dfのage列の平均値を確認 4 | -------------------------------------------------------------------------------- /input/q_067.txt: -------------------------------------------------------------------------------- 1 | [question67] 2 | 3 | # dfのage列の中央値を確認 4 | -------------------------------------------------------------------------------- /input/q_068.txt: -------------------------------------------------------------------------------- 1 | [question68] 2 | 3 | # ①df2の生徒ごとの合計点（行方向の合計） 4 | # ②df2の科目ごとの点数の総和（列方向の合計） 5 | -------------------------------------------------------------------------------- /input/q_069.txt: -------------------------------------------------------------------------------- 1 | [question69] 2 | 3 | # df2のEnglishで得点の最大値 4 | -------------------------------------------------------------------------------- /input/q_070.txt: -------------------------------------------------------------------------------- 1 | [question70] 2 | 3 | # df2のEnglishで得点の最小値 4 | -------------------------------------------------------------------------------- /input/q_071.txt: -------------------------------------------------------------------------------- 1 | [question71] 2 | 3 | # df2においてclassでグルーピングし、クラスごとの科目の 4 | # 最大値、最小値、平均値を求める(name列は削除しておく) 5 | -------------------------------------------------------------------------------- /input/q_072.txt: -------------------------------------------------------------------------------- 1 | [question72] 2 | 3 | # dfの基本統計量を確認(describe) 4 | -------------------------------------------------------------------------------- /input/q_073.txt: -------------------------------------------------------------------------------- 1 | [question73] 2 | 3 | # dfの各列間の(Pearson)相関係数を確認 4 | -------------------------------------------------------------------------------- /input/q_074.txt: -------------------------------------------------------------------------------- 1 | [question74] 2 | 3 | # scikit-learnを用いてdf2のEnglish、Mathmatics、History列を標準化する 4 | # (from sklearn.preprocessing import StandardScalerをインポート) 5 | -------------------------------------------------------------------------------- /input/q_075.txt: -------------------------------------------------------------------------------- 1 | [question75] 2 | 3 | # scikit-learnを用いてdf2のEnglish列を標準化する 4 | # (from sklearn.preprocessing import StandardScalerをインポート) 5 | -------------------------------------------------------------------------------- /input/q_076.txt: -------------------------------------------------------------------------------- 1 | [question76] 2 | 3 | # scikit-learnを用いてdf2のEnglish、Mathmatics、History列を 4 | # Min-Maxスケーリングする 5 | # (from sklearn.preprocessing import StandardScalerをインポート) 6 | -------------------------------------------------------------------------------- /input/q_077.txt: -------------------------------------------------------------------------------- 1 | [question77] 2 | 3 | # dfのfare列の最大値、最小値の行名を取得 4 | -------------------------------------------------------------------------------- /input/q_078.txt: -------------------------------------------------------------------------------- 1 | [question78] 2 | 3 | # dfのfare列の0、25、50、75、100パーセンタイルを取得 4 | -------------------------------------------------------------------------------- /input/q_079.txt: -------------------------------------------------------------------------------- 1 | [question79] 2 | 3 | # ①dfのage列の最頻値を取得 4 | # ②value_counts()にてage列の要素数を 5 | # 確認し、①の結果の妥当性を確認 6 | -------------------------------------------------------------------------------- /input/q_080.txt: -------------------------------------------------------------------------------- 1 | [question80] 2 | 3 | # dfのsex列をラベルエンコーディングし、 4 | # dfの先頭5行を表示 5 | # (from sklearn.preprocessing import LabelEncoderをインポート) 6 | -------------------------------------------------------------------------------- /input/q_081.txt: -------------------------------------------------------------------------------- 1 | [question81] 2 | 3 | # dfのsex列をOne-hotエンコーディングし、 4 | # dfの先頭5行を表示 5 | -------------------------------------------------------------------------------- /input/q_082.txt: -------------------------------------------------------------------------------- 1 | [question82] 2 | 3 | # dfのすべての数値列のヒストグラムを表示 4 | -------------------------------------------------------------------------------- /input/q_083.txt: -------------------------------------------------------------------------------- 1 | [question83] 2 | 3 | # dfのage列をヒストグラムで表示 4 | -------------------------------------------------------------------------------- /input/q_084.txt: -------------------------------------------------------------------------------- 1 | [question84] 2 | 3 | # df2のname列の要素ごとの3科目合計得点を棒グラフで表示 4 | -------------------------------------------------------------------------------- /input/q_085.txt: -------------------------------------------------------------------------------- 1 | [question85] 2 | 3 | # df2のname列の要素ごとの3科目を棒グラフで 4 | # 並べて表示 5 | -------------------------------------------------------------------------------- /input/q_086.txt: -------------------------------------------------------------------------------- 1 | [question86] 2 | 3 | # df2のname列の要素ごとの3科目を積み上げ棒グラフで表示 4 | -------------------------------------------------------------------------------- /input/q_087.txt: -------------------------------------------------------------------------------- 1 | [question87] 2 | 3 | # dfの各列間の散布図を表示 4 | # (from pandas.plotting import scatter_matrixをインポート) 5 | -------------------------------------------------------------------------------- /input/q_088.txt: -------------------------------------------------------------------------------- 1 | [question88] 2 | 3 | # dfのage列とfare列で散布図を作成 4 | -------------------------------------------------------------------------------- /input/q_089.txt: -------------------------------------------------------------------------------- 1 | [question89] 2 | 3 | # 【88】で描画したグラフに「age-fare scatter」という 4 | # グラフタイトルをつける 5 | -------------------------------------------------------------------------------- /input/titanic3.csv: -------------------------------------------------------------------------------- 1 | "pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" 2 | 1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" 3 | 1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" 4 | 1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" 5 | 1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" 6 | 1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" 7 | 1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" 8 | 1,1,"Andrews, Miss. Kornelia Theodosia","female",63,1,0,"13502",77.9583,"D7","S","10",,"Hudson, NY" 9 | 1,0,"Andrews, Mr. Thomas Jr","male",39,0,0,"112050",0.0000,"A36","S",,,"Belfast, NI" 10 | 1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)","female",53,2,0,"11769",51.4792,"C101","S","D",,"Bayside, Queens, NY" 11 | 1,0,"Artagaveytia, Mr. Ramon","male",71,0,0,"PC 17609",49.5042,,"C",,"22","Montevideo, Uruguay" 12 | 1,0,"Astor, Col. John Jacob","male",47,1,0,"PC 17757",227.5250,"C62 C64","C",,"124","New York, NY" 13 | 1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)","female",18,1,0,"PC 17757",227.5250,"C62 C64","C","4",,"New York, NY" 14 | 1,1,"Aubart, Mme. Leontine Pauline","female",24,0,0,"PC 17477",69.3000,"B35","C","9",,"Paris, France" 15 | 1,1,"Barber, Miss. Ellen ""Nellie""","female",26,0,0,"19877",78.8500,,"S","6",, 16 | 1,1,"Barkworth, Mr. Algernon Henry Wilson","male",80,0,0,"27042",30.0000,"A23","S","B",,"Hessle, Yorks" 17 | 1,0,"Baumann, Mr. John D","male",,0,0,"PC 17318",25.9250,,"S",,,"New York, NY" 18 | 1,0,"Baxter, Mr. Quigg Edmond","male",24,0,1,"PC 17558",247.5208,"B58 B60","C",,,"Montreal, PQ" 19 | 1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)","female",50,0,1,"PC 17558",247.5208,"B58 B60","C","6",,"Montreal, PQ" 20 | 1,1,"Bazzani, Miss. Albina","female",32,0,0,"11813",76.2917,"D15","C","8",, 21 | 1,0,"Beattie, Mr. Thomson","male",36,0,0,"13050",75.2417,"C6","C","A",,"Winnipeg, MN" 22 | 1,1,"Beckwith, Mr. Richard Leonard","male",37,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" 23 | 1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)","female",47,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" 24 | 1,1,"Behr, Mr. Karl Howell","male",26,0,0,"111369",30.0000,"C148","C","5",,"New York, NY" 25 | 1,1,"Bidois, Miss. Rosalie","female",42,0,0,"PC 17757",227.5250,,"C","4",, 26 | 1,1,"Bird, Miss. Ellen","female",29,0,0,"PC 17483",221.7792,"C97","S","8",, 27 | 1,0,"Birnbaum, Mr. Jakob","male",25,0,0,"13905",26.0000,,"C",,"148","San Francisco, CA" 28 | 1,1,"Bishop, Mr. Dickinson H","male",25,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" 29 | 1,1,"Bishop, Mrs. Dickinson H (Helen Walton)","female",19,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" 30 | 1,1,"Bissette, Miss. Amelia","female",35,0,0,"PC 17760",135.6333,"C99","S","8",, 31 | 1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan","male",28,0,0,"110564",26.5500,"C52","S","D",,"Stockholm, Sweden / Washington, DC" 32 | 1,0,"Blackwell, Mr. Stephen Weart","male",45,0,0,"113784",35.5000,"T","S",,,"Trenton, NJ" 33 | 1,1,"Blank, Mr. Henry","male",40,0,0,"112277",31.0000,"A31","C","7",,"Glen Ridge, NJ" 34 | 1,1,"Bonnell, Miss. Caroline","female",30,0,0,"36928",164.8667,"C7","S","8",,"Youngstown, OH" 35 | 1,1,"Bonnell, Miss. Elizabeth","female",58,0,0,"113783",26.5500,"C103","S","8",,"Birkdale, England Cleveland, Ohio" 36 | 1,0,"Borebank, Mr. John James","male",42,0,0,"110489",26.5500,"D22","S",,,"London / Winnipeg, MB" 37 | 1,1,"Bowen, Miss. Grace Scott","female",45,0,0,"PC 17608",262.3750,,"C","4",,"Cooperstown, NY" 38 | 1,1,"Bowerman, Miss. Elsie Edith","female",22,0,1,"113505",55.0000,"E33","S","6",,"St Leonards-on-Sea, England Ohio" 39 | 1,1,"Bradley, Mr. George (""George Arthur Brayton"")","male",,0,0,"111427",26.5500,,"S","9",,"Los Angeles, CA" 40 | 1,0,"Brady, Mr. John Bertram","male",41,0,0,"113054",30.5000,"A21","S",,,"Pomeroy, WA" 41 | 1,0,"Brandeis, Mr. Emil","male",48,0,0,"PC 17591",50.4958,"B10","C",,"208","Omaha, NE" 42 | 1,0,"Brewe, Dr. Arthur Jackson","male",,0,0,"112379",39.6000,,"C",,,"Philadelphia, PA" 43 | 1,1,"Brown, Mrs. James Joseph (Margaret Tobin)","female",44,0,0,"PC 17610",27.7208,"B4","C","6",,"Denver, CO" 44 | 1,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)","female",59,2,0,"11769",51.4792,"C101","S","D",,"Belmont, MA" 45 | 1,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)","female",60,0,0,"11813",76.2917,"D15","C","8",,"Philadelphia, PA" 46 | 1,1,"Burns, Miss. Elizabeth Margaret","female",41,0,0,"16966",134.5000,"E40","C","3",, 47 | 1,0,"Butt, Major. Archibald Willingham","male",45,0,0,"113050",26.5500,"B38","S",,,"Washington, DC" 48 | 1,0,"Cairns, Mr. Alexander","male",,0,0,"113798",31.0000,,"S",,, 49 | 1,1,"Calderhead, Mr. Edward Pennington","male",42,0,0,"PC 17476",26.2875,"E24","S","5",,"New York, NY" 50 | 1,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)","female",53,0,0,"PC 17606",27.4458,,"C","6",,"Washington, DC" 51 | 1,1,"Cardeza, Mr. Thomas Drake Martinez","male",36,0,1,"PC 17755",512.3292,"B51 B53 B55","C","3",,"Austria-Hungary / Germantown, Philadelphia, PA" 52 | 1,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)","female",58,0,1,"PC 17755",512.3292,"B51 B53 B55","C","3",,"Germantown, Philadelphia, PA" 53 | 1,0,"Carlsson, Mr. Frans Olof","male",33,0,0,"695",5.0000,"B51 B53 B55","S",,,"New York, NY" 54 | 1,0,"Carrau, Mr. Francisco M","male",28,0,0,"113059",47.1000,,"S",,,"Montevideo, Uruguay" 55 | 1,0,"Carrau, Mr. Jose Pedro","male",17,0,0,"113059",47.1000,,"S",,,"Montevideo, Uruguay" 56 | 1,1,"Carter, Master. William Thornton II","male",11,1,2,"113760",120.0000,"B96 B98","S","4",,"Bryn Mawr, PA" 57 | 1,1,"Carter, Miss. Lucile Polk","female",14,1,2,"113760",120.0000,"B96 B98","S","4",,"Bryn Mawr, PA" 58 | 1,1,"Carter, Mr. William Ernest","male",36,1,2,"113760",120.0000,"B96 B98","S","C",,"Bryn Mawr, PA" 59 | 1,1,"Carter, Mrs. William Ernest (Lucile Polk)","female",36,1,2,"113760",120.0000,"B96 B98","S","4",,"Bryn Mawr, PA" 60 | 1,0,"Case, Mr. Howard Brown","male",49,0,0,"19924",26.0000,,"S",,,"Ascot, Berkshire / Rochester, NY" 61 | 1,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)","female",,0,0,"17770",27.7208,,"C","5",,"New York, NY" 62 | 1,0,"Cavendish, Mr. Tyrell William","male",36,1,0,"19877",78.8500,"C46","S",,"172","Little Onn Hall, Staffs" 63 | 1,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)","female",76,1,0,"19877",78.8500,"C46","S","6",,"Little Onn Hall, Staffs" 64 | 1,0,"Chaffee, Mr. Herbert Fuller","male",46,1,0,"W.E.P. 5734",61.1750,"E31","S",,,"Amenia, ND" 65 | 1,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)","female",47,1,0,"W.E.P. 5734",61.1750,"E31","S","4",,"Amenia, ND" 66 | 1,1,"Chambers, Mr. Norman Campbell","male",27,1,0,"113806",53.1000,"E8","S","5",,"New York, NY / Ithaca, NY" 67 | 1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)","female",33,1,0,"113806",53.1000,"E8","S","5",,"New York, NY / Ithaca, NY" 68 | 1,1,"Chaudanson, Miss. Victorine","female",36,0,0,"PC 17608",262.3750,"B61","C","4",, 69 | 1,1,"Cherry, Miss. Gladys","female",30,0,0,"110152",86.5000,"B77","S","8",,"London, England" 70 | 1,1,"Chevre, Mr. Paul Romaine","male",45,0,0,"PC 17594",29.7000,"A9","C","7",,"Paris, France" 71 | 1,1,"Chibnall, Mrs. (Edith Martha Bowerman)","female",,0,1,"113505",55.0000,"E33","S","6",,"St Leonards-on-Sea, England Ohio" 72 | 1,0,"Chisholm, Mr. Roderick Robert Crispin","male",,0,0,"112051",0.0000,,"S",,,"Liverpool, England / Belfast" 73 | 1,0,"Clark, Mr. Walter Miller","male",27,1,0,"13508",136.7792,"C89","C",,,"Los Angeles, CA" 74 | 1,1,"Clark, Mrs. Walter Miller (Virginia McDowell)","female",26,1,0,"13508",136.7792,"C89","C","4",,"Los Angeles, CA" 75 | 1,1,"Cleaver, Miss. Alice","female",22,0,0,"113781",151.5500,,"S","11",, 76 | 1,0,"Clifford, Mr. George Quincy","male",,0,0,"110465",52.0000,"A14","S",,,"Stoughton, MA" 77 | 1,0,"Colley, Mr. Edward Pomeroy","male",47,0,0,"5727",25.5875,"E58","S",,,"Victoria, BC" 78 | 1,1,"Compton, Miss. Sara Rebecca","female",39,1,1,"PC 17756",83.1583,"E49","C","14",,"Lakewood, NJ" 79 | 1,0,"Compton, Mr. Alexander Taylor Jr","male",37,1,1,"PC 17756",83.1583,"E52","C",,,"Lakewood, NJ" 80 | 1,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)","female",64,0,2,"PC 17756",83.1583,"E45","C","14",,"Lakewood, NJ" 81 | 1,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)","female",55,2,0,"11770",25.7000,"C101","S","2",,"New York, NY" 82 | 1,0,"Crafton, Mr. John Bertram","male",,0,0,"113791",26.5500,,"S",,,"Roachdale, IN" 83 | 1,0,"Crosby, Capt. Edward Gifford","male",70,1,1,"WE/P 5735",71.0000,"B22","S",,"269","Milwaukee, WI" 84 | 1,1,"Crosby, Miss. 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Catharina","female",10,0,2,"345773",24.1500,,"S",,, 1268 | 3,0,"Van Impe, Mr. Jean Baptiste","male",36,1,1,"345773",24.1500,,"S",,, 1269 | 3,0,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)","female",30,1,1,"345773",24.1500,,"S",,, 1270 | 3,0,"van Melkebeke, Mr. Philemon","male",,0,0,"345777",9.5000,,"S",,, 1271 | 3,0,"Vande Velde, Mr. Johannes Joseph","male",33,0,0,"345780",9.5000,,"S",,, 1272 | 3,0,"Vande Walle, Mr. Nestor Cyriel","male",28,0,0,"345770",9.5000,,"S",,, 1273 | 3,0,"Vanden Steen, Mr. Leo Peter","male",28,0,0,"345783",9.5000,,"S",,, 1274 | 3,0,"Vander Cruyssen, Mr. Victor","male",47,0,0,"345765",9.0000,,"S",,, 1275 | 3,0,"Vander Planke, Miss. Augusta Maria","female",18,2,0,"345764",18.0000,,"S",,, 1276 | 3,0,"Vander Planke, Mr. Julius","male",31,3,0,"345763",18.0000,,"S",,, 1277 | 3,0,"Vander Planke, Mr. Leo Edmondus","male",16,2,0,"345764",18.0000,,"S",,, 1278 | 3,0,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)","female",31,1,0,"345763",18.0000,,"S",,, 1279 | 3,1,"Vartanian, Mr. David","male",22,0,0,"2658",7.2250,,"C","13 15",, 1280 | 3,0,"Vendel, Mr. Olof Edvin","male",20,0,0,"350416",7.8542,,"S",,, 1281 | 3,0,"Vestrom, Miss. Hulda Amanda Adolfina","female",14,0,0,"350406",7.8542,,"S",,, 1282 | 3,0,"Vovk, Mr. Janko","male",22,0,0,"349252",7.8958,,"S",,, 1283 | 3,0,"Waelens, Mr. Achille","male",22,0,0,"345767",9.0000,,"S",,,"Antwerp, Belgium / Stanton, OH" 1284 | 3,0,"Ware, Mr. Frederick","male",,0,0,"359309",8.0500,,"S",,, 1285 | 3,0,"Warren, Mr. Charles William","male",,0,0,"C.A. 49867",7.5500,,"S",,, 1286 | 3,0,"Webber, Mr. James","male",,0,0,"SOTON/OQ 3101316",8.0500,,"S",,, 1287 | 3,0,"Wenzel, Mr. Linhart","male",32.5,0,0,"345775",9.5000,,"S",,"298", 1288 | 3,1,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)","female",38,0,0,"2688",7.2292,,"C","C",, 1289 | 3,0,"Widegren, Mr. Carl/Charles Peter","male",51,0,0,"347064",7.7500,,"S",,, 1290 | 3,0,"Wiklund, Mr. Jakob Alfred","male",18,1,0,"3101267",6.4958,,"S",,"314", 1291 | 3,0,"Wiklund, Mr. Karl Johan","male",21,1,0,"3101266",6.4958,,"S",,, 1292 | 3,1,"Wilkes, Mrs. James (Ellen Needs)","female",47,1,0,"363272",7.0000,,"S",,, 1293 | 3,0,"Willer, Mr. Aaron (""Abi Weller"")","male",,0,0,"3410",8.7125,,"S",,, 1294 | 3,0,"Willey, Mr. Edward","male",,0,0,"S.O./P.P. 751",7.5500,,"S",,, 1295 | 3,0,"Williams, Mr. Howard Hugh ""Harry""","male",,0,0,"A/5 2466",8.0500,,"S",,, 1296 | 3,0,"Williams, Mr. Leslie","male",28.5,0,0,"54636",16.1000,,"S",,"14", 1297 | 3,0,"Windelov, Mr. Einar","male",21,0,0,"SOTON/OQ 3101317",7.2500,,"S",,, 1298 | 3,0,"Wirz, Mr. Albert","male",27,0,0,"315154",8.6625,,"S",,"131", 1299 | 3,0,"Wiseman, Mr. Phillippe","male",,0,0,"A/4. 34244",7.2500,,"S",,, 1300 | 3,0,"Wittevrongel, Mr. Camille","male",36,0,0,"345771",9.5000,,"S",,, 1301 | 3,0,"Yasbeck, Mr. Antoni","male",27,1,0,"2659",14.4542,,"C","C",, 1302 | 3,1,"Yasbeck, Mrs. Antoni (Selini Alexander)","female",15,1,0,"2659",14.4542,,"C",,, 1303 | 3,0,"Youseff, Mr. Gerious","male",45.5,0,0,"2628",7.2250,,"C",,"312", 1304 | 3,0,"Yousif, Mr. Wazli","male",,0,0,"2647",7.2250,,"C",,, 1305 | 3,0,"Yousseff, Mr. Gerious","male",,0,0,"2627",14.4583,,"C",,, 1306 | 3,0,"Zabour, Miss. Hileni","female",14.5,1,0,"2665",14.4542,,"C",,"328", 1307 | 3,0,"Zabour, Miss. Thamine","female",,1,0,"2665",14.4542,,"C",,, 1308 | 3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", 1309 | 3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, 1310 | 3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, 1311 | -------------------------------------------------------------------------------- /notebook/01_Pandas_100_Knocks_for_Begginer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 初学者向けPandas100本ノックver1.0.7\n", 8 | "##### Shift + Enter で各セルのコードが実行されます\n", 9 | "・print(ans[ 問題番号 ]) で回答コード例を表示 \n", 10 | "・Python3エンジニア認定データ分析試験にも沿った問題内容 \n", 11 | "・Pandasの各メソッドの説明は以下のサイトが分かりやすいです \n", 12 | " 　[note.nkmk.me](https://note.nkmk.me/pandas/) \n", 13 | "・\"Data for Titanic passengers\" from [VANDERBILT UNIVERSITY](https://biostat.app.vumc.org/wiki/Main/DataSets) \n", 14 | " \n", 15 | "**本コンテンツ作成時のpandasのバージョンは1.1.0**\n", 16 | " \n", 17 | "作成日:2020年9月24日 \n", 18 | "最終更新日:2023年1月16日 \n", 19 | "再配布・改編不可 \n", 20 | "作成者：[kunishou](https://qiita.com/kunishou)" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "# Shift + Enterで題材データ、回答コードを読み込んで下さい\n", 30 | "\n", 31 | "import pandas as pd\n", 32 | "import glob\n", 33 | "\n", 34 | "#題材データをdfに読み込み(タイタニック号の乗客データ、テストの点数データ 等)\n", 35 | "def initialize1():\n", 36 | " df = pd.read_csv('../input/titanic3.csv')\n", 37 | " return df\n", 38 | "\n", 39 | "def initialize2():\n", 40 | " df = pd.read_csv('../input/data1.csv')\n", 41 | " return df\n", 42 | "\n", 43 | "df = initialize1()\n", 44 | "df2 = initialize2()\n", 45 | "df3 = pd.read_csv('../input/data1_2.csv')\n", 46 | "df4 = pd.read_csv('../input/data1_3.csv')\n", 47 | "df5 = pd.read_csv('../input/data2.csv',encoding='cp932')\n", 48 | "\n", 49 | "#回答コードをansリストに格納\n", 50 | "path = sorted(glob.glob('../input/a_' + '*.txt'))\n", 51 | "\n", 52 | "ans = []\n", 53 | "\n", 54 | "for _ in range(len(path)):\n", 55 | " with open(path[_], 'r',encoding='utf-8') as f:\n", 56 | " ans.append(f.read())\n", 57 | " \n", 58 | "print(ans[0])" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "# Pandas基礎 (1 - 13)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "# 【1】\n", 75 | "# dfに読み込んだデータの最初の5行を表示\n", 76 | "#print(ans[1]) #回答表示\n", 77 | "df = initialize1() #初期化\n", 78 | "\n", 79 | "\n", 80 | "\n" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": null, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "# 【2】\n", 90 | "# dfに読み込んだデータの最後の5行を表示\n", 91 | "#print(ans[2]) #回答表示\n", 92 | "df = initialize1() #初期化\n", 93 | "\n", 94 | "\n", 95 | "\n" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# 【3】\n", 105 | "# dfのDataFrameサイズを確認\n", 106 | "#print(ans[3]) #回答表示\n", 107 | "df = initialize1() #初期化\n", 108 | "\n", 109 | "\n", 110 | "\n" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": null, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "# 【4】\n", 120 | "# inputフォルダ内のdata1.csvファイルを\n", 121 | "# 読み込みdf2に格納して、最初の5行を表示\n", 122 | "#print(ans[4]) #回答表示\n", 123 | "\n", 124 | "\n", 125 | "\n" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "# 【5】\n", 135 | "# dfのfareの列で昇順に並び替えて表示\n", 136 | "#print(ans[5]) #回答表示\n", 137 | "df = initialize1() #初期化\n", 138 | "\n", 139 | "\n", 140 | "\n" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": null, 146 | "metadata": {}, 147 | "outputs": [], 148 | "source": [ 149 | "# 【6】\n", 150 | "# df_copyにdfをコピーして、最初の5行を表示\n", 151 | "#print(ans[6]) #回答表示\n", 152 | "df = initialize1() #初期化\n", 153 | "\n", 154 | "\n", 155 | "\n" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": null, 161 | "metadata": {}, 162 | "outputs": [], 163 | "source": [ 164 | "# 【7】\n", 165 | "# ① dfの各列のデータ型を確認\n", 166 | "# ② dfのcabinの列のデータ型を確認\n", 167 | "#print(ans[7]) #回答表示\n", 168 | "df = initialize1() #初期化\n", 169 | "\n", 170 | "\n", 171 | "\n" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "# 【8】\n", 181 | "# ① dfのpclassの列のデータ型をdtypeで確認\n", 182 | "# ② 数値型から文字型に変換し、データ型をdtypeで確認\n", 183 | "#print(ans[8]) #回答表示\n", 184 | "df = initialize1() #初期化\n", 185 | "\n", 186 | "\n", 187 | "\n" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": null, 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "# 【9】\n", 197 | "# dfのレコード数(行数)を確認\n", 198 | "#print(ans[9]) #回答表示\n", 199 | "df = initialize1() #初期化\n", 200 | "\n", 201 | "\n", 202 | "\n" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "# 【10】\n", 212 | "# dfのレコード数(行数)、各列のデータ型、欠損値の有無を確認\n", 213 | "#print(ans[10]) #回答表示\n", 214 | "df = initialize1() #初期化\n", 215 | "\n", 216 | "\n", 217 | "\n" 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": null, 223 | "metadata": {}, 224 | "outputs": [], 225 | "source": [ 226 | "# 【11】\n", 227 | "# dfのsex,cabinの列の要素を確認\n", 228 | "#print(ans[11]) #回答表示\n", 229 | "df = initialize1() #初期化\n", 230 | "\n", 231 | "\n", 232 | "\n" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": {}, 239 | "outputs": [], 240 | "source": [ 241 | "# 【12】\n", 242 | "# dfの列名一覧をlist形式で表示\n", 243 | "#print(ans[12]) #回答表示\n", 244 | "df = initialize1() #初期化\n", 245 | "\n", 246 | "\n", 247 | "\n" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": null, 253 | "metadata": {}, 254 | "outputs": [], 255 | "source": [ 256 | "# 【13】\n", 257 | "# dfのインデックス一覧をndaaray形式で表示\n", 258 | "#print(ans[13]) #回答表示\n", 259 | "df = initialize1() #初期化#\n", 260 | "\n", 261 | "\n", 262 | "\n" 263 | ] 264 | }, 265 | { 266 | "cell_type": "markdown", 267 | "metadata": {}, 268 | "source": [ 269 | "# データ抽出 (14 - 32)" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": null, 275 | "metadata": {}, 276 | "outputs": [], 277 | "source": [ 278 | "# 【14】\n", 279 | "# dfのnameの列のみ表示\n", 280 | "#print(ans[14]) #回答表示\n", 281 | "df = initialize1() #初期化\n", 282 | "\n", 283 | "\n", 284 | "\n" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": null, 290 | "metadata": {}, 291 | "outputs": [], 292 | "source": [ 293 | "# 【15】\n", 294 | "# dfのnameとsexの列のみ表示\n", 295 | "#print(ans[15]) #回答表示\n", 296 | "df = initialize1() #初期化\n", 297 | "\n", 298 | "\n", 299 | "\n" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": null, 305 | "metadata": {}, 306 | "outputs": [], 307 | "source": [ 308 | "# 【16】\n", 309 | "# dfのindex(行)の4行目までを表示\n", 310 | "#print(ans[16]) #回答表示\n", 311 | "df = initialize1() #初期化\n", 312 | "\n", 313 | "\n", 314 | "\n" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": null, 320 | "metadata": {}, 321 | "outputs": [], 322 | "source": [ 323 | "# 【17】\n", 324 | "# dfのindex(行)の4行目から10行目までを表示\n", 325 | "#print(ans[17]) #回答表示\n", 326 | "df = initialize1() #初期化\n", 327 | "\n", 328 | "\n", 329 | "\n" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": {}, 336 | "outputs": [], 337 | "source": [ 338 | "# 【18】\n", 339 | "# locを使ってdf全体を表示\n", 340 | "#print(ans[18]) #回答表示\n", 341 | "df = initialize1() #初期化\n", 342 | "\n", 343 | "\n", 344 | "\n" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "metadata": {}, 351 | "outputs": [], 352 | "source": [ 353 | "# 【19】\n", 354 | "# locを使ってdfのfare列をすべて表示\n", 355 | "#print(ans[19]) #回答表示\n", 356 | "df = initialize1() #初期化\n", 357 | "\n", 358 | "\n", 359 | "\n" 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": null, 365 | "metadata": {}, 366 | "outputs": [], 367 | "source": [ 368 | "# 【20】\n", 369 | "# locを使ってdfのfare列の10のラベルまで表示\n", 370 | "#print(ans[20]) #回答表示\n", 371 | "df = initialize1() #初期化\n", 372 | "\n", 373 | "\n", 374 | "\n" 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": null, 380 | "metadata": { 381 | "scrolled": true 382 | }, 383 | "outputs": [], 384 | "source": [ 385 | "# 【21】\n", 386 | "# locを使ってdfのnameとticketの列をすべて表示\n", 387 | "#print(ans[21]) #回答表示\n", 388 | "df = initialize1() #初期化\n", 389 | "\n", 390 | "\n", 391 | "\n" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": null, 397 | "metadata": {}, 398 | "outputs": [], 399 | "source": [ 400 | "# 【22】\n", 401 | "# locを使ってdfのnameからcabinまでの列をすべて表示\n", 402 | "#print(ans[22]) #回答表示\n", 403 | "df = initialize1() #初期化\n", 404 | "\n", 405 | "\n", 406 | "\n" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": null, 412 | "metadata": {}, 413 | "outputs": [], 414 | "source": [ 415 | "# 【23】\n", 416 | "# ilocを使ってdfのage列を5行目まで表示\n", 417 | "#print(ans[23]) #回答表示\n", 418 | "df = initialize1() #初期化\n", 419 | "\n", 420 | "\n", 421 | "\n" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": null, 427 | "metadata": {}, 428 | "outputs": [], 429 | "source": [ 430 | "# 【24】\n", 431 | "# dfのname,age,sexの列のみ抽出しdf_copyに格納\n", 432 | "# その後outputフォルダにcsvファイルで出力\n", 433 | "# 出力ファイル名はsample.csv\n", 434 | "#print(ans[24]) #回答表示\n", 435 | "df = initialize1() #初期化\n", 436 | "\n", 437 | "\n", 438 | "\n" 439 | ] 440 | }, 441 | { 442 | "cell_type": "code", 443 | "execution_count": null, 444 | "metadata": {}, 445 | "outputs": [], 446 | "source": [ 447 | "# 【25】\n", 448 | "# dfのage列の値が30以上のデータのみ抽出\n", 449 | "#print(ans[25]) #回答表示\n", 450 | "df = initialize1() #初期化\n", 451 | "\n", 452 | "\n", 453 | "\n" 454 | ] 455 | }, 456 | { 457 | "cell_type": "code", 458 | "execution_count": null, 459 | "metadata": {}, 460 | "outputs": [], 461 | "source": [ 462 | "# 【26】\n", 463 | "# dfのsex列がfemaleのデータのみ抽出\n", 464 | "#print(ans[26]) #回答表示\n", 465 | "df = initialize1() #初期化\n", 466 | "\n", 467 | "\n", 468 | "\n" 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": null, 474 | "metadata": {}, 475 | "outputs": [], 476 | "source": [ 477 | "# 【27】\n", 478 | "# dfのsex列がfemaleでかつageが40以上のデータのみ抽出\n", 479 | "#print(ans[27]) #回答表示\n", 480 | "df = initialize1() #初期化\n", 481 | "\n", 482 | "\n", 483 | "\n" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": null, 489 | "metadata": {}, 490 | "outputs": [], 491 | "source": [ 492 | "# 【28】\n", 493 | "# queryを用いてdfのsex列がfemaleでかつageが40以上のデータのみ抽出\n", 494 | "#print(ans[28]) #回答表示\n", 495 | "df = initialize1() #初期化\n", 496 | "\n", 497 | "\n", 498 | "\n" 499 | ] 500 | }, 501 | { 502 | "cell_type": "code", 503 | "execution_count": null, 504 | "metadata": {}, 505 | "outputs": [], 506 | "source": [ 507 | "# 【29】\n", 508 | "# dfのname列に文字列「Mrs」が含まれるデータを表示\n", 509 | "#print(ans[29]) #回答表示\n", 510 | "df = initialize1() #初期化\n", 511 | "\n", 512 | "\n", 513 | "\n" 514 | ] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": null, 519 | "metadata": {}, 520 | "outputs": [], 521 | "source": [ 522 | "# 【30】\n", 523 | "# dfの中で文字列型の列のみを表示\n", 524 | "#print(ans[30]) #回答表示\n", 525 | "df = initialize1() #初期化\n", 526 | "\n", 527 | "\n", 528 | "\n" 529 | ] 530 | }, 531 | { 532 | "cell_type": "code", 533 | "execution_count": null, 534 | "metadata": {}, 535 | "outputs": [], 536 | "source": [ 537 | "# 【31】\n", 538 | "# dfの各列の要素数の確認\n", 539 | "#print(ans[31]) #回答表示\n", 540 | "df = initialize1() #初期化\n", 541 | "\n", 542 | "\n", 543 | "\n" 544 | ] 545 | }, 546 | { 547 | "cell_type": "code", 548 | "execution_count": null, 549 | "metadata": {}, 550 | "outputs": [], 551 | "source": [ 552 | "# 【32】\n", 553 | "# dfのembarked列の要素と出現回数の確認\n", 554 | "#print(ans[32]) #回答表示\n", 555 | "df = initialize1() #初期化\n", 556 | "\n", 557 | "\n", 558 | "\n" 559 | ] 560 | }, 561 | { 562 | "cell_type": "markdown", 563 | "metadata": {}, 564 | "source": [ 565 | "# データ加工 (33 - 58)" 566 | ] 567 | }, 568 | { 569 | "cell_type": "code", 570 | "execution_count": null, 571 | "metadata": {}, 572 | "outputs": [], 573 | "source": [ 574 | "# 【33】\n", 575 | "# dfのindex名が「3」のage列を\n", 576 | "# 30から40に変更し、先頭の5行を表示\n", 577 | "#print(ans[33]) #回答表示\n", 578 | "df = initialize1() #初期化\n", 579 | "\n", 580 | "\n", 581 | "\n" 582 | ] 583 | }, 584 | { 585 | "cell_type": "code", 586 | "execution_count": null, 587 | "metadata": {}, 588 | "outputs": [], 589 | "source": [ 590 | "# 【34】\n", 591 | "# dfのsex列にてmale→0、femlae→1に\n", 592 | "# 変更し、先頭の5行を表示\n", 593 | "#print(ans[34]) #回答表示\n", 594 | "df = initialize1() #初期化\n", 595 | "\n", 596 | "\n", 597 | "\n" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": null, 603 | "metadata": { 604 | "scrolled": true 605 | }, 606 | "outputs": [], 607 | "source": [ 608 | "# 【35】\n", 609 | "# dfのfare列に100を足して、\n", 610 | "# 先頭の5行を表示\n", 611 | "#print(ans[35]) #回答表示\n", 612 | "df = initialize1() #初期化\n", 613 | "\n", 614 | "\n", 615 | "\n" 616 | ] 617 | }, 618 | { 619 | "cell_type": "code", 620 | "execution_count": null, 621 | "metadata": {}, 622 | "outputs": [], 623 | "source": [ 624 | "# 【36】\n", 625 | "# dfのfare列に2を掛けて、\n", 626 | "# 先頭の5行を表示\n", 627 | "#print(ans[36]) #回答表示\n", 628 | "df = initialize1() #初期化\n", 629 | "\n", 630 | "\n", 631 | "\n" 632 | ] 633 | }, 634 | { 635 | "cell_type": "code", 636 | "execution_count": null, 637 | "metadata": {}, 638 | "outputs": [], 639 | "source": [ 640 | "# 【37】\n", 641 | "# dfのfare列を小数点以下で丸めて、先頭の5行を表示\n", 642 | "#print(ans[37]) #回答表示\n", 643 | "df = initialize1() #初期化\n", 644 | "\n", 645 | "\n", 646 | "\n" 647 | ] 648 | }, 649 | { 650 | "cell_type": "code", 651 | "execution_count": null, 652 | "metadata": {}, 653 | "outputs": [], 654 | "source": [ 655 | "# 【38】\n", 656 | "# dfに列名「test」で値がすべて1の\n", 657 | "# カラムを追加し、先頭の5行を表示\n", 658 | "#print(ans[38]) #回答表示\n", 659 | "df = initialize1() #初期化\n", 660 | "\n", 661 | "\n", 662 | "\n" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": null, 668 | "metadata": {}, 669 | "outputs": [], 670 | "source": [ 671 | "# 【39】\n", 672 | "# dfにcabinとembarkedの列を「_」で\n", 673 | "# 結合した列を追加(列名は「test」)し、\n", 674 | "# 先頭の5行を表示\n", 675 | "#print(ans[39]) #回答表示\n", 676 | "df = initialize1() #初期化\n", 677 | "\n", 678 | "\n", 679 | "\n" 680 | ] 681 | }, 682 | { 683 | "cell_type": "code", 684 | "execution_count": null, 685 | "metadata": {}, 686 | "outputs": [], 687 | "source": [ 688 | "# 【40】\n", 689 | "# dfにageとembarkedの列を「_」で\n", 690 | "# 結合した列を追加(列名は「test」)し、\n", 691 | "# 先頭の5行を表示\n", 692 | "#print(ans[40]) #回答表示\n", 693 | "df = initialize1() #初期化\n", 694 | "\n", 695 | "\n", 696 | "\n" 697 | ] 698 | }, 699 | { 700 | "cell_type": "code", 701 | "execution_count": null, 702 | "metadata": {}, 703 | "outputs": [], 704 | "source": [ 705 | "# 【41】\n", 706 | "# dfからbodyの列を削除し、最初の5行を表示\n", 707 | "#print(ans[41]) #回答表示\n", 708 | "df = initialize1() #初期化\n", 709 | "\n", 710 | "\n", 711 | "\n" 712 | ] 713 | }, 714 | { 715 | "cell_type": "code", 716 | "execution_count": null, 717 | "metadata": {}, 718 | "outputs": [], 719 | "source": [ 720 | "# 【42】\n", 721 | "# dfからインデックス名「3」の行を削除し、最初の5行を表示\n", 722 | "#print(ans[42]) #回答表示\n", 723 | "df = initialize1() #初期化\n", 724 | "\n", 725 | "\n", 726 | "\n" 727 | ] 728 | }, 729 | { 730 | "cell_type": "code", 731 | "execution_count": null, 732 | "metadata": {}, 733 | "outputs": [], 734 | "source": [ 735 | "# 【43】\n", 736 | "# df2の列名を'name', 'class', 'Biology', 'Physics', 'Chemistry'に変更\n", 737 | "# df2の最初の5行を表示\n", 738 | "#print(ans[43]) #回答表示\n", 739 | "df2 = initialize2() #初期化\n", 740 | "\n", 741 | "\n", 742 | "\n" 743 | ] 744 | }, 745 | { 746 | "cell_type": "code", 747 | "execution_count": null, 748 | "metadata": {}, 749 | "outputs": [], 750 | "source": [ 751 | "# 【44】\n", 752 | "# df2の列名を'English'をBiology'に変更\n", 753 | "# df2の最初の5行を表示\n", 754 | "#print(ans[44]) #回答表示\n", 755 | "df2 = initialize2() #初期化\n", 756 | "\n", 757 | "\n", 758 | "\n", 759 | "\n" 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "execution_count": null, 765 | "metadata": {}, 766 | "outputs": [], 767 | "source": [ 768 | "# 【45】\n", 769 | "# df2のインデックス名「1」を「10」に変更\n", 770 | "# df2の最初の5行を表示\n", 771 | "#print(ans[45]) #回答表示\n", 772 | "df2 = initialize2() #初期化\n", 773 | "\n", 774 | "\n", 775 | "\n" 776 | ] 777 | }, 778 | { 779 | "cell_type": "code", 780 | "execution_count": null, 781 | "metadata": {}, 782 | "outputs": [], 783 | "source": [ 784 | "# 【46】\n", 785 | "# dfのすべての列の欠損値数を確認\n", 786 | "#print(ans[46]) #回答表示\n", 787 | "df = initialize1() #初期化\n", 788 | "\n", 789 | "\n", 790 | "\n" 791 | ] 792 | }, 793 | { 794 | "cell_type": "code", 795 | "execution_count": null, 796 | "metadata": {}, 797 | "outputs": [], 798 | "source": [ 799 | "# 【47】\n", 800 | "# dfのage列の欠損値に30を代入\n", 801 | "# その後、ageの欠損値数を確認\n", 802 | "#print(ans[47]) #回答表示\n", 803 | "df = initialize1() #初期化\n", 804 | "\n", 805 | "\n", 806 | "\n" 807 | ] 808 | }, 809 | { 810 | "cell_type": "code", 811 | "execution_count": null, 812 | "metadata": {}, 813 | "outputs": [], 814 | "source": [ 815 | "# 【48】\n", 816 | "# dfでひとつでも欠損値がある行を削除\n", 817 | "# その後、dfの欠損値数を確認\n", 818 | "#print(ans[48]) #回答表示\n", 819 | "df = initialize1() #初期化\n", 820 | "\n", 821 | "\n", 822 | "\n" 823 | ] 824 | }, 825 | { 826 | "cell_type": "code", 827 | "execution_count": null, 828 | "metadata": {}, 829 | "outputs": [], 830 | "source": [ 831 | "# 【49】\n", 832 | "# dfのsurvivedの列をndarray形式(配列)で表示\n", 833 | "#print(ans[49]) #回答表示\n", 834 | "df = initialize1() #初期化\n", 835 | "\n", 836 | "\n", 837 | "\n" 838 | ] 839 | }, 840 | { 841 | "cell_type": "code", 842 | "execution_count": null, 843 | "metadata": {}, 844 | "outputs": [], 845 | "source": [ 846 | "# 【50】\n", 847 | "# dfの行をシャッフルして表示\n", 848 | "#print(ans[50]) #回答表示\n", 849 | "df = initialize1() #初期化\n", 850 | "\n", 851 | "\n", 852 | "\n" 853 | ] 854 | }, 855 | { 856 | "cell_type": "code", 857 | "execution_count": null, 858 | "metadata": {}, 859 | "outputs": [], 860 | "source": [ 861 | "# 【51】\n", 862 | "# dfの行をシャッフルし、インデックスを振り直して表示\n", 863 | "#print(ans[51]) #回答表示\n", 864 | "df = initialize1() #初期化\n", 865 | "\n", 866 | "\n", 867 | "\n" 868 | ] 869 | }, 870 | { 871 | "cell_type": "code", 872 | "execution_count": null, 873 | "metadata": {}, 874 | "outputs": [], 875 | "source": [ 876 | "# 【52】\n", 877 | "# ①df2の重複行数をカウント\n", 878 | "# ②df2の重複行を削除し、df2を表示\n", 879 | "#print(ans[52]) #回答表示\n", 880 | "df2 = initialize2() #初期化\n", 881 | "\n", 882 | "\n", 883 | "\n" 884 | ] 885 | }, 886 | { 887 | "cell_type": "code", 888 | "execution_count": null, 889 | "metadata": {}, 890 | "outputs": [], 891 | "source": [ 892 | "# 【53】\n", 893 | "# dfのnameの列をすべて大文字に変換し表示\n", 894 | "#print(ans[53]) #回答表示\n", 895 | "df = initialize1() #初期化\n", 896 | "\n", 897 | "\n", 898 | "\n" 899 | ] 900 | }, 901 | { 902 | "cell_type": "code", 903 | "execution_count": null, 904 | "metadata": {}, 905 | "outputs": [], 906 | "source": [ 907 | "# 【54】\n", 908 | "# dfのnameの列をすべて小文字に変換し表示\n", 909 | "#print(ans[54]) #回答表示\n", 910 | "df = initialize1() #初期化\n", 911 | "\n", 912 | "\n", 913 | "\n" 914 | ] 915 | }, 916 | { 917 | "cell_type": "code", 918 | "execution_count": null, 919 | "metadata": {}, 920 | "outputs": [], 921 | "source": [ 922 | "# 【55】\n", 923 | "# dfのsex列に含まれる「female」という単語を\n", 924 | "# 「Python」に置換。その後、1行目の\n", 925 | "# 「female」が「Python」に置き換わったことを確認\n", 926 | "#print(ans[55]) #回答表示\n", 927 | "df = initialize1() #初期化\n", 928 | "\n", 929 | "\n", 930 | "\n" 931 | ] 932 | }, 933 | { 934 | "cell_type": "code", 935 | "execution_count": null, 936 | "metadata": {}, 937 | "outputs": [], 938 | "source": [ 939 | "# 【56】\n", 940 | "# dfのname列1行目の「Allen, Miss. Elisabeth Walton」の\n", 941 | "# 「Elisabeth」を消去(import reをインポート)\n", 942 | "#print(ans[56]) #回答表示\n", 943 | "df = initialize1() #初期化\n", 944 | "\n", 945 | "\n", 946 | "\n" 947 | ] 948 | }, 949 | { 950 | "cell_type": "code", 951 | "execution_count": null, 952 | "metadata": {}, 953 | "outputs": [], 954 | "source": [ 955 | "# 【57】\n", 956 | "# df5の都道府県列と市区町村列を空白がないように\n", 957 | "# 「_」で結合(新規列名は「test2」)し、先頭5行を表示\n", 958 | "# ※df5の「test」列は通常通り結合した場合の結果\n", 959 | "#print(ans[57]) #回答表示\n", 960 | "\n", 961 | "\n", 962 | "\n" 963 | ] 964 | }, 965 | { 966 | "cell_type": "code", 967 | "execution_count": null, 968 | "metadata": {}, 969 | "outputs": [], 970 | "source": [ 971 | "# 【58】\n", 972 | "# df2の行と列を入れ替えて表示\n", 973 | "#print(ans[58]) #回答表示\n", 974 | "df2 = initialize2() #初期化\n", 975 | "\n", 976 | "\n", 977 | "\n" 978 | ] 979 | }, 980 | { 981 | "cell_type": "markdown", 982 | "metadata": {}, 983 | "source": [ 984 | "# マージと連結(59 - 65)" 985 | ] 986 | }, 987 | { 988 | "cell_type": "code", 989 | "execution_count": null, 990 | "metadata": {}, 991 | "outputs": [], 992 | "source": [ 993 | "# 【59】\n", 994 | "# df2にdf3を左結合（結合キーはname）し、df2に格納\n", 995 | "#print(ans[59]) #回答表示\n", 996 | "df2 = initialize2() #初期化\n", 997 | "\n", 998 | "\n", 999 | "\n" 1000 | ] 1001 | }, 1002 | { 1003 | "cell_type": "code", 1004 | "execution_count": null, 1005 | "metadata": {}, 1006 | "outputs": [], 1007 | "source": [ 1008 | "# 【60】\n", 1009 | "# df2にdf3を右結合（結合キーはname）し、df2に格納\n", 1010 | "#print(ans[60]) #回答表示\n", 1011 | "df2 = initialize2() #初期化\n", 1012 | "\n", 1013 | "\n", 1014 | "\n" 1015 | ] 1016 | }, 1017 | { 1018 | "cell_type": "code", 1019 | "execution_count": null, 1020 | "metadata": {}, 1021 | "outputs": [], 1022 | "source": [ 1023 | "# 【61】\n", 1024 | "# df2にdf3を内部結合（結合キーはname）し、df2に格納\n", 1025 | "#print(ans[61]) #回答表示\n", 1026 | "df2 = initialize2() #初期化\n", 1027 | "\n", 1028 | "\n", 1029 | "\n" 1030 | ] 1031 | }, 1032 | { 1033 | "cell_type": "code", 1034 | "execution_count": null, 1035 | "metadata": {}, 1036 | "outputs": [], 1037 | "source": [ 1038 | "# 【62】\n", 1039 | "# df2にdf3を外部結合し、df2に格納\n", 1040 | "#print(ans[62]) #回答表示\n", 1041 | "df2 = initialize2() #初期化\n", 1042 | "\n", 1043 | "\n", 1044 | "\n" 1045 | ] 1046 | }, 1047 | { 1048 | "cell_type": "code", 1049 | "execution_count": null, 1050 | "metadata": {}, 1051 | "outputs": [], 1052 | "source": [ 1053 | "# 【63】\n", 1054 | "# df2とdf4を列方向に連結し、df2に格納\n", 1055 | "#print(ans[63]) #回答表示\n", 1056 | "df2 = initialize2() #初期化\n", 1057 | "\n", 1058 | "\n", 1059 | "\n" 1060 | ] 1061 | }, 1062 | { 1063 | "cell_type": "code", 1064 | "execution_count": null, 1065 | "metadata": {}, 1066 | "outputs": [], 1067 | "source": [ 1068 | "# 【64】\n", 1069 | "# df2とdf4を列方向に連結後、重複している\n", 1070 | "# name列の片方を削除し、df2に格納\n", 1071 | "#print(ans[64]) #回答表示\n", 1072 | "df2 = initialize2() #初期化\n", 1073 | "\n", 1074 | "\n", 1075 | "\n" 1076 | ] 1077 | }, 1078 | { 1079 | "cell_type": "code", 1080 | "execution_count": null, 1081 | "metadata": {}, 1082 | "outputs": [], 1083 | "source": [ 1084 | "# 【65】\n", 1085 | "# df2とdf4を行方向に連結し、df2に格納\n", 1086 | "#print(ans[65]) #回答表示\n", 1087 | "df2 = initialize2() #初期化\n", 1088 | "\n", 1089 | "\n", 1090 | "\n" 1091 | ] 1092 | }, 1093 | { 1094 | "cell_type": "markdown", 1095 | "metadata": {}, 1096 | "source": [ 1097 | "# 統計 (66 - 79)" 1098 | ] 1099 | }, 1100 | { 1101 | "cell_type": "code", 1102 | "execution_count": null, 1103 | "metadata": {}, 1104 | "outputs": [], 1105 | "source": [ 1106 | "# 【66】\n", 1107 | "# dfのage列の平均値を確認\n", 1108 | "#print(ans[66]) #回答表示\n", 1109 | "df = initialize1() #初期化\n", 1110 | "\n", 1111 | "\n", 1112 | "\n" 1113 | ] 1114 | }, 1115 | { 1116 | "cell_type": "code", 1117 | "execution_count": null, 1118 | "metadata": {}, 1119 | "outputs": [], 1120 | "source": [ 1121 | "# 【67】\n", 1122 | "# dfのage列の中央値を確認\n", 1123 | "#print(ans[67]) #回答表示\n", 1124 | "df = initialize1() #初期化\n", 1125 | "\n", 1126 | "\n", 1127 | "\n" 1128 | ] 1129 | }, 1130 | { 1131 | "cell_type": "code", 1132 | "execution_count": null, 1133 | "metadata": {}, 1134 | "outputs": [], 1135 | "source": [ 1136 | "# 【68】\n", 1137 | "# ①df2の生徒ごとの合計点（行方向の合計）\n", 1138 | "# ②df2の科目ごとの点数の総和（列方向の合計）\n", 1139 | "#print(ans[68]) #回答表示\n", 1140 | "df2 = initialize2() #初期化\n", 1141 | "\n", 1142 | "\n", 1143 | "\n" 1144 | ] 1145 | }, 1146 | { 1147 | "cell_type": "code", 1148 | "execution_count": null, 1149 | "metadata": {}, 1150 | "outputs": [], 1151 | "source": [ 1152 | "# 【69】\n", 1153 | "# df2のEnglishで得点の最大値\n", 1154 | "#print(ans[69]) #回答表示\n", 1155 | "df2 = initialize2() #初期化\n", 1156 | "\n", 1157 | "\n", 1158 | "\n" 1159 | ] 1160 | }, 1161 | { 1162 | "cell_type": "code", 1163 | "execution_count": null, 1164 | "metadata": {}, 1165 | "outputs": [], 1166 | "source": [ 1167 | "# 【70】\n", 1168 | "# df2のEnglishで得点の最小値\n", 1169 | "#print(ans[70]) #回答表示\n", 1170 | "df2 = initialize2() #初期化\n", 1171 | "\n", 1172 | "\n", 1173 | "\n" 1174 | ] 1175 | }, 1176 | { 1177 | "cell_type": "code", 1178 | "execution_count": null, 1179 | "metadata": {}, 1180 | "outputs": [], 1181 | "source": [ 1182 | "# 【71】\n", 1183 | "# df2においてclassでグルーピングし、クラスごとの科目の\n", 1184 | "# 最大値、最小値、平均値を求める(name列は削除しておく)\n", 1185 | "#print(ans[71]) #回答表示\n", 1186 | "df2 = initialize2() #初期化\n", 1187 | "\n", 1188 | "\n", 1189 | "\n" 1190 | ] 1191 | }, 1192 | { 1193 | "cell_type": "code", 1194 | "execution_count": null, 1195 | "metadata": {}, 1196 | "outputs": [], 1197 | "source": [ 1198 | "# 【72】\n", 1199 | "# dfの基本統計量を確認(describe)\n", 1200 | "#print(ans[72]) #回答表示\n", 1201 | "df = initialize1() #初期化\n", 1202 | "\n", 1203 | "\n", 1204 | "\n" 1205 | ] 1206 | }, 1207 | { 1208 | "cell_type": "code", 1209 | "execution_count": null, 1210 | "metadata": {}, 1211 | "outputs": [], 1212 | "source": [ 1213 | "# 【73】\n", 1214 | "# dfの各列間の(Pearson)相関係数を確認\n", 1215 | "#print(ans[73]) #回答表示\n", 1216 | "df = initialize1() #初期化\n", 1217 | "\n", 1218 | "\n", 1219 | "\n" 1220 | ] 1221 | }, 1222 | { 1223 | "cell_type": "code", 1224 | "execution_count": null, 1225 | "metadata": {}, 1226 | "outputs": [], 1227 | "source": [ 1228 | "# 【74】\n", 1229 | "# scikit-learnを用いてdf2のEnglish、Mathematics、History列を標準化する\n", 1230 | "# (from sklearn.preprocessing import StandardScalerをインポート)\n", 1231 | "#print(ans[74]) #回答表示\n", 1232 | "df2 = initialize2() #初期化\n", 1233 | "\n", 1234 | "\n", 1235 | "\n" 1236 | ] 1237 | }, 1238 | { 1239 | "cell_type": "code", 1240 | "execution_count": null, 1241 | "metadata": {}, 1242 | "outputs": [], 1243 | "source": [ 1244 | "# 【75】\n", 1245 | "# scikit-learnを用いてdf2のEnglish列を標準化する\n", 1246 | "# (from sklearn.preprocessing import StandardScalerをインポート)\n", 1247 | "#print(ans[75]) #回答表示\n", 1248 | "df2 = initialize2() #初期化\n", 1249 | "\n", 1250 | "\n", 1251 | "\n" 1252 | ] 1253 | }, 1254 | { 1255 | "cell_type": "code", 1256 | "execution_count": null, 1257 | "metadata": {}, 1258 | "outputs": [], 1259 | "source": [ 1260 | "# 【76】\n", 1261 | "# scikit-learnを用いてdf2のEnglish、Mathematics、History列を\n", 1262 | "# Min-Maxスケーリングする\n", 1263 | "# (from sklearn.preprocessing import MinMaxScalerをインポート)\n", 1264 | "#print(ans[76]) #回答表示\n", 1265 | "df2 = initialize2() #初期化\n", 1266 | "\n", 1267 | "\n", 1268 | "\n" 1269 | ] 1270 | }, 1271 | { 1272 | "cell_type": "code", 1273 | "execution_count": null, 1274 | "metadata": {}, 1275 | "outputs": [], 1276 | "source": [ 1277 | "# 【77】\n", 1278 | "# dfのfare列の最大値、最小値の行名を取得\n", 1279 | "#print(ans[77]) #回答表示\n", 1280 | "df = initialize1() #初期化\n", 1281 | "\n", 1282 | "\n", 1283 | "\n" 1284 | ] 1285 | }, 1286 | { 1287 | "cell_type": "code", 1288 | "execution_count": null, 1289 | "metadata": {}, 1290 | "outputs": [], 1291 | "source": [ 1292 | "# 【78】\n", 1293 | "# dfのfare列の0、25、50、75、100パーセンタイルを取得\n", 1294 | "#print(ans[78]) #回答表示\n", 1295 | "df = initialize1() #初期化\n", 1296 | "\n", 1297 | "\n", 1298 | "\n" 1299 | ] 1300 | }, 1301 | { 1302 | "cell_type": "code", 1303 | "execution_count": null, 1304 | "metadata": {}, 1305 | "outputs": [], 1306 | "source": [ 1307 | "# 【79】\n", 1308 | "# ①dfのage列の最頻値を取得\n", 1309 | "# ②value_counts()にてage列の要素数を\n", 1310 | "# 確認し、①の結果の妥当性を確認\n", 1311 | "#print(ans[79]) #回答表示\n", 1312 | "df = initialize1() #初期化\n", 1313 | "\n", 1314 | "\n", 1315 | "\n" 1316 | ] 1317 | }, 1318 | { 1319 | "cell_type": "markdown", 1320 | "metadata": {}, 1321 | "source": [ 1322 | "# ラベリング (80 - 81)" 1323 | ] 1324 | }, 1325 | { 1326 | "cell_type": "code", 1327 | "execution_count": null, 1328 | "metadata": {}, 1329 | "outputs": [], 1330 | "source": [ 1331 | "# 【80】\n", 1332 | "# dfのsex列をラベルエンコーディングし、\n", 1333 | "# dfの先頭5行を表示\n", 1334 | "# (from sklearn.preprocessing import LabelEncoderをインポート)\n", 1335 | "#print(ans[80]) #回答表示\n", 1336 | "df = initialize1() #初期化\n", 1337 | "\n", 1338 | "\n", 1339 | "\n" 1340 | ] 1341 | }, 1342 | { 1343 | "cell_type": "code", 1344 | "execution_count": null, 1345 | "metadata": {}, 1346 | "outputs": [], 1347 | "source": [ 1348 | "# 【81】\n", 1349 | "# dfのsex列をOne-hotエンコーディングし、\n", 1350 | "# dfの先頭5行を表示\n", 1351 | "#print(ans[81]) #回答表示\n", 1352 | "df = initialize1() #初期化\n", 1353 | "\n", 1354 | "\n", 1355 | "\n" 1356 | ] 1357 | }, 1358 | { 1359 | "cell_type": "markdown", 1360 | "metadata": {}, 1361 | "source": [ 1362 | "# Pandasプロット (82 - 89)\n", 1363 | "Pandasプロットの機能については以下のサイトの説明が分かりやすいです \n", 1364 | "[自調自考の旅](https://own-search-and-study.xyz/2016/08/03/pandas%E3%81%AEplot%E3%81%AE%E5%85%A8%E5%BC%95%E6%95%B0%E3%82%92%E4%BD%BF%E3%81%84%E3%81%93%E3%81%AA%E3%81%99/)" 1365 | ] 1366 | }, 1367 | { 1368 | "cell_type": "code", 1369 | "execution_count": null, 1370 | "metadata": {}, 1371 | "outputs": [], 1372 | "source": [ 1373 | "# 【82】\n", 1374 | "# dfのすべての数値列のヒストグラムを表示\n", 1375 | "#print(ans[82]) #回答表示\n", 1376 | "df = initialize1() #初期化\n", 1377 | "\n", 1378 | "\n", 1379 | "\n" 1380 | ] 1381 | }, 1382 | { 1383 | "cell_type": "code", 1384 | "execution_count": null, 1385 | "metadata": {}, 1386 | "outputs": [], 1387 | "source": [ 1388 | "# 【83】\n", 1389 | "# dfのage列をヒストグラムで表示\n", 1390 | "#print(ans[83]) #回答表示\n", 1391 | "df = initialize1() #初期化\n", 1392 | "\n", 1393 | "\n", 1394 | "\n" 1395 | ] 1396 | }, 1397 | { 1398 | "cell_type": "code", 1399 | "execution_count": null, 1400 | "metadata": {}, 1401 | "outputs": [], 1402 | "source": [ 1403 | "# 【84】\n", 1404 | "# df2のname列の要素ごとの3科目合計得点を棒グラフで表示\n", 1405 | "#print(ans[84]) #回答表示\n", 1406 | "df2 = initialize2() #初期化\n", 1407 | "\n", 1408 | "\n", 1409 | "\n" 1410 | ] 1411 | }, 1412 | { 1413 | "cell_type": "code", 1414 | "execution_count": null, 1415 | "metadata": {}, 1416 | "outputs": [], 1417 | "source": [ 1418 | "# 【85】\n", 1419 | "# df2のname列の要素ごとの3科目を棒グラフで\n", 1420 | "# 並べて表示\n", 1421 | "#print(ans[85]) #回答表示\n", 1422 | "df2 = initialize2() #初期化\n", 1423 | "\n", 1424 | "\n", 1425 | "\n" 1426 | ] 1427 | }, 1428 | { 1429 | "cell_type": "code", 1430 | "execution_count": null, 1431 | "metadata": {}, 1432 | "outputs": [], 1433 | "source": [ 1434 | "# 【86】\n", 1435 | "# df2のname列の要素ごとの3科目を積み上げ棒グラフで表示\n", 1436 | "#print(ans[86]) #回答表示\n", 1437 | "df2 = initialize2() #初期化\n", 1438 | "\n", 1439 | "\n", 1440 | "\n" 1441 | ] 1442 | }, 1443 | { 1444 | "cell_type": "code", 1445 | "execution_count": null, 1446 | "metadata": {}, 1447 | "outputs": [], 1448 | "source": [ 1449 | "# 【87】\n", 1450 | "# dfの各列間の散布図を表示\n", 1451 | "# (from pandas.plotting import scatter_matrixをインポート)\n", 1452 | "#print(ans[87]) #回答表示\n", 1453 | "df = initialize1() #初期化\n", 1454 | "\n", 1455 | "\n", 1456 | "\n" 1457 | ] 1458 | }, 1459 | { 1460 | "cell_type": "code", 1461 | "execution_count": null, 1462 | "metadata": {}, 1463 | "outputs": [], 1464 | "source": [ 1465 | "# 【88】\n", 1466 | "# dfのage列とfare列で散布図を作成\n", 1467 | "#print(ans[88]) #回答表示\n", 1468 | "df = initialize1() #初期化\n", 1469 | "\n", 1470 | "\n", 1471 | "\n" 1472 | ] 1473 | }, 1474 | { 1475 | "cell_type": "code", 1476 | "execution_count": null, 1477 | "metadata": {}, 1478 | "outputs": [], 1479 | "source": [ 1480 | "# 【89】\n", 1481 | "# 【88】で描画したグラフに「age-fare scatter」という\n", 1482 | "# グラフタイトルをつける\n", 1483 | "#print(ans[89]) #回答表示\n", 1484 | "df = initialize1() #初期化\n", 1485 | "\n", 1486 | "\n", 1487 | "\n" 1488 | ] 1489 | }, 1490 | { 1491 | "cell_type": "markdown", 1492 | "metadata": {}, 1493 | "source": [ 1494 | "# タイタニック号の生存者予測 (90 - 100) \n", 1495 | "これまで触れてきたタイタニック号の乗客データを使用して、乗客の生存有無を \n", 1496 | "予測してみます。 \n", 1497 | " \n", 1498 | "※90～100については順番通りにやらないと上手く動作しません" 1499 | ] 1500 | }, 1501 | { 1502 | "cell_type": "code", 1503 | "execution_count": null, 1504 | "metadata": {}, 1505 | "outputs": [], 1506 | "source": [ 1507 | "# 【90】ラベルエンコーディング\n", 1508 | "# df_copyのsexとembarked列をラベルエンコーディング\n", 1509 | "# (from sklearn.preprocessing import LabelEncoderをインポート)\n", 1510 | "# (df_copyはdfをコピーしたもの)\n", 1511 | "#print(ans[90]) #回答表示\n", 1512 | "df_copy =df.copy()\n", 1513 | "\n", 1514 | "\n", 1515 | "\n" 1516 | ] 1517 | }, 1518 | { 1519 | "cell_type": "code", 1520 | "execution_count": null, 1521 | "metadata": {}, 1522 | "outputs": [], 1523 | "source": [ 1524 | "# 【91】欠損値確認\n", 1525 | "# df_copyの欠損値を確認\n", 1526 | "#print(ans[91]) #回答表示\n", 1527 | "\n", 1528 | "\n", 1529 | "\n" 1530 | ] 1531 | }, 1532 | { 1533 | "cell_type": "code", 1534 | "execution_count": null, 1535 | "metadata": {}, 1536 | "outputs": [], 1537 | "source": [ 1538 | "# 【92】欠損値補完\n", 1539 | "# df_copyのage、fare列の欠損値を各列の平均値で補完\n", 1540 | "#print(ans[92]) #回答表示\n", 1541 | "\n", 1542 | "\n", 1543 | "\n" 1544 | ] 1545 | }, 1546 | { 1547 | "cell_type": "code", 1548 | "execution_count": null, 1549 | "metadata": {}, 1550 | "outputs": [], 1551 | "source": [ 1552 | "# 【93】不要列の削除\n", 1553 | "# df_copyの中で機械学習で使用しない不要な行を削除\n", 1554 | "# (name, ticket, cabin, boat, body, home.destを削除)\n", 1555 | "#print(ans[93]) #回答表示\n", 1556 | "\n", 1557 | "\n", 1558 | "\n" 1559 | ] 1560 | }, 1561 | { 1562 | "cell_type": "code", 1563 | "execution_count": null, 1564 | "metadata": {}, 1565 | "outputs": [], 1566 | "source": [ 1567 | "# 【94】ndarray形式への変換\n", 1568 | "# ①df_copyのpclass、age、sex、fare、embarkedの列を抽出し、ndarray形式に変換\n", 1569 | "# ②df_copyのsurvivedの列を抽出し、ndarray形式に変換\n", 1570 | "# (①をfeatures、②をtargetという変数にそれぞれ格納)\n", 1571 | "#print(ans[94]) #回答表示\n", 1572 | "\n", 1573 | "\n", 1574 | "\n" 1575 | ] 1576 | }, 1577 | { 1578 | "cell_type": "code", 1579 | "execution_count": null, 1580 | "metadata": {}, 1581 | "outputs": [], 1582 | "source": [ 1583 | "# 【95】学習データとテストデータに分割\n", 1584 | "# 【94】で作成したfeatrues、targetを学習データとテストデータに分割\n", 1585 | "# (from sklearn.model_selection import train_test_splitをインポート)\n", 1586 | "# ※分割時のパラメータは次を指定 test_size=0.3 random_state=0\n", 1587 | "#print(ans[95]) #回答表示\n", 1588 | "\n", 1589 | "\n", 1590 | "\n" 1591 | ] 1592 | }, 1593 | { 1594 | "cell_type": "code", 1595 | "execution_count": null, 1596 | "metadata": {}, 1597 | "outputs": [], 1598 | "source": [ 1599 | "# 【96】学習の実行\n", 1600 | "# 学習データ(features、target)を用いランダムフォレストにて学習を実行\n", 1601 | "# (from sklearn.ensemble import RandomForestClassifierをインポート)\n", 1602 | "# ※パラメータは次を指定 n_estimators=100 random_state=0\n", 1603 | "#print(ans[96]) #回答表示\n", 1604 | "\n", 1605 | "\n", 1606 | "\n" 1607 | ] 1608 | }, 1609 | { 1610 | "cell_type": "code", 1611 | "execution_count": null, 1612 | "metadata": {}, 1613 | "outputs": [], 1614 | "source": [ 1615 | "# 【97】予測の実行\n", 1616 | "# test_Xデータの乗客の生存を予測\n", 1617 | "#print(ans[97]) #回答表示\n", 1618 | "\n", 1619 | "\n", 1620 | "\n" 1621 | ] 1622 | }, 1623 | { 1624 | "cell_type": "code", 1625 | "execution_count": null, 1626 | "metadata": {}, 1627 | "outputs": [], 1628 | "source": [ 1629 | "# 【98】予測精度の確認\n", 1630 | "# 予測結果がtest_y(生存有無の答え)とどれぐらい\n", 1631 | "# 整合していたかを確認(評価指標はaccuracy)\n", 1632 | "# (from sklearn.metrics import accuracy_scoreをインポート)\n", 1633 | "#print(ans[98]) #回答表示\n", 1634 | "\n", 1635 | "\n", 1636 | "\n" 1637 | ] 1638 | }, 1639 | { 1640 | "cell_type": "code", 1641 | "execution_count": null, 1642 | "metadata": {}, 1643 | "outputs": [], 1644 | "source": [ 1645 | "# 【99】重要度の確認\n", 1646 | "# 学習における各列(特徴量)の\n", 1647 | "# 重要度を表示\n", 1648 | "#print(ans[99]) #回答表示\n", 1649 | "\n", 1650 | "\n", 1651 | "\n" 1652 | ] 1653 | }, 1654 | { 1655 | "cell_type": "code", 1656 | "execution_count": null, 1657 | "metadata": {}, 1658 | "outputs": [], 1659 | "source": [ 1660 | "# 【100】予測結果のcsv出力\n", 1661 | "# test_Xの予測結果をcsvでoutputフォルダに出力(ファイル名は「submission.csv」)\n", 1662 | "# (headerは不要)\n", 1663 | "#print(ans[100]) #回答表示\n", 1664 | "\n", 1665 | "\n", 1666 | "\n" 1667 | ] 1668 | }, 1669 | { 1670 | "cell_type": "markdown", 1671 | "metadata": {}, 1672 | "source": [ 1673 | "# ノックお疲れ様でした\n", 1674 | "各セルのoutput表示をすべてクリアにしたい場合は、ツールバーの \n", 1675 | "「Kernel」→「Restart & Clear Output」を実行して下さい。" 1676 | ] 1677 | } 1678 | ], 1679 | "metadata": { 1680 | "kernelspec": { 1681 | "display_name": "Python 3", 1682 | "language": "python", 1683 | "name": "python3" 1684 | }, 1685 | "language_info": { 1686 | "codemirror_mode": { 1687 | "name": "ipython", 1688 | "version": 3 1689 | }, 1690 | "file_extension": ".py", 1691 | "mimetype": "text/x-python", 1692 | "name": "python", 1693 | "nbconvert_exporter": "python", 1694 | "pygments_lexer": "ipython3", 1695 | "version": "3.7.6" 1696 | } 1697 | }, 1698 | "nbformat": 4, 1699 | "nbformat_minor": 4 1700 | } 1701 | -------------------------------------------------------------------------------- /notebook/02_Pandas_100_Knocks_for_Begginer_Random_Knocks.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 【ランダムノック】初学者向けPandas100本ノックver1.0.7\n", 8 | "##### 1～89までの問題をランダムに表示\n", 9 | "##### Shift + Enter で各セルのコードが実行されます\n", 10 | "・print(ans[ 問題番号 ]) で回答コード例を表示 \n", 11 | "・Python3エンジニア認定データ分析試験にも沿った問題内容 \n", 12 | "・Pandasの各メソッドの説明は以下のサイトが分かりやすいです \n", 13 | " 　[note.nkmk.me](https://note.nkmk.me/pandas/) \n", 14 | "・\"Data for Titanic passengers\" from [VANDERBILT UNIVERSITY](https://biostat.app.vumc.org/wiki/Main/DataSets) \n", 15 | " \n", 16 | "**本コンテンツ作成時のpandasのバージョンは1.1.0**\n", 17 | " \n", 18 | "作成日:2020年9月24日 \n", 19 | "最終更新日:203年1月16日 \n", 20 | "再配布・改編不可 \n", 21 | "作成者：[kunishou](https://qiita.com/kunishou)" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": null, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "# Shift + Enterで題材データ、回答コードを読み込んで下さい\n", 31 | "\n", 32 | "import pandas as pd\n", 33 | "import glob\n", 34 | "import random\n", 35 | "\n", 36 | "#題材データをdfに読み込み(タイタニック号の乗客データ、テストの点数データ 等)\n", 37 | "def initialize1():\n", 38 | " df = pd.read_csv('../input/titanic3.csv')\n", 39 | " return df\n", 40 | "\n", 41 | "def initialize2():\n", 42 | " df = pd.read_csv('../input/data1.csv')\n", 43 | " return df\n", 44 | "\n", 45 | "df = initialize1()\n", 46 | "df2 = initialize2()\n", 47 | "df3 = pd.read_csv('../input/data1_2.csv')\n", 48 | "df4 = pd.read_csv('../input/data1_3.csv')\n", 49 | "df5 = pd.read_csv('../input/data2.csv',encoding='cp932')\n", 50 | "\n", 51 | "#問題文をquesリストに格納\n", 52 | "path = sorted(glob.glob('../input/q_' + '*.txt'))\n", 53 | "\n", 54 | "ques = []\n", 55 | "\n", 56 | "for _ in range(len(path)):\n", 57 | " with open(path[_], 'r',encoding='utf-8') as f:\n", 58 | " ques.append(f.read())\n", 59 | "\n", 60 | "#回答コードをansリストに格納\n", 61 | "path2 = sorted(glob.glob('../input/a_' + '*.txt'))\n", 62 | "\n", 63 | "ans = []\n", 64 | "\n", 65 | "for _ in range(len(path)):\n", 66 | " with open(path2[_], 'r',encoding='utf-8') as f:\n", 67 | " ans.append(f.read())\n", 68 | "\n", 69 | "print(ques[0])\n", 70 | "print(ans[0])" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "# Random Knocks" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "#問題をランダムに表示\n", 87 | "print(random.choice(ques[1:]))" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": null, 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "df = initialize1() # 初期化\n", 97 | "df2 = initialize2() # 初期化\n", 98 | "# 回答をここに記述\n", 99 | "\n", 100 | "\n", 101 | "\n" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "#print(ans[]) # 回答表示(ans[]の[]内に問題番号を記入)" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "各セルのoutput表示をすべてクリアにしたい場合は、ツールバーの \n", 118 | "「Kernel」→「Restart & Clear Output」を実行して下さい。" 119 | ] 120 | } 121 | ], 122 | "metadata": { 123 | "kernelspec": { 124 | "display_name": "Python 3", 125 | "language": "python", 126 | "name": "python3" 127 | }, 128 | "language_info": { 129 | "codemirror_mode": { 130 | "name": "ipython", 131 | "version": 3 132 | }, 133 | "file_extension": ".py", 134 | "mimetype": "text/x-python", 135 | "name": "python", 136 | "nbconvert_exporter": "python", 137 | "pygments_lexer": "ipython3", 138 | "version": "3.7.6" 139 | } 140 | }, 141 | "nbformat": 4, 142 | "nbformat_minor": 4 143 | } 144 | -------------------------------------------------------------------------------- /notebook/03_Titanic_Passengers_Prediction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# タイタニック号乗客の生存予測ver1.0.7\n", 8 | "##### 90～100を通しで実行する場合のサンプルコード\n", 9 | "##### Shift + Enter で各セルのコードが実行されます\n", 10 | "・print(ans[ 問題番号 ]) で回答コード例を表示 \n", 11 | "・Python3エンジニア認定データ分析試験にも沿った問題内容 \n", 12 | "・Pandasの各メソッドの説明は以下のサイトが分かりやすいです \n", 13 | " 　[note.nkmk.me](https://note.nkmk.me/pandas/) \n", 14 | "・\"Data for Titanic passengers\" from [VANDERBILT UNIVERSITY](https://biostat.app.vumc.org/wiki/Main/DataSets) \n", 15 | " \n", 16 | "**本コンテンツ作成時のpandasのバージョンは1.1.0**\n", 17 | " \n", 18 | "作成日:2020年9月24日 \n", 19 | "再配布・改編不可 \n", 20 | "作成者：[kunishou](https://qiita.com/kunishou)" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "import pandas as pd\n", 30 | "\n", 31 | "# データの読み込み\n", 32 | "df = pd.read_csv('../input/titanic3.csv')\n", 33 | "\n", 34 | "df_copy = df.copy()\n", 35 | "\n", 36 | "# ラベルエンコーディング\n", 37 | "from sklearn.preprocessing import LabelEncoder\n", 38 | "\n", 39 | "le = LabelEncoder() #ラベルエンコーダのインスタンスを作成\n", 40 | "\n", 41 | "df_copy['sex'] = le.fit_transform(df_copy['sex']) #エンコーディング\n", 42 | "df_copy['embarked'] = le.fit_transform(df_copy['embarked'].astype(str))\n", 43 | "\n", 44 | "# 欠損値補完\n", 45 | "df_copy['age'] = df_copy['age'].fillna(df_copy['age'].median()) #欠損値にageの平均値で補完\n", 46 | "df_copy['fare'] = df_copy['fare'].fillna(df_copy['fare'].median()) #欠損値にfareの平均値で補完\n", 47 | "\n", 48 | "# 不要行の削除\n", 49 | "df_copy = df_copy.drop(['name', 'ticket', 'cabin', 'boat', 'body', 'home.dest'],axis=1)\n", 50 | "\n", 51 | "# ndarray形式への変換\n", 52 | "features = df_copy[['pclass','age','sex','fare','embarked']].values\n", 53 | "target = df_copy['survived'].values\n", 54 | "\n", 55 | "# 学習データとテストデータに分割\n", 56 | "from sklearn.model_selection import train_test_split\n", 57 | "\n", 58 | "(features , test_X , target , test_y) = train_test_split(features, target , test_size = 0.3 , random_state = 0)\n", 59 | "\n", 60 | "# 学習\n", 61 | "from sklearn.ensemble import RandomForestClassifier\n", 62 | "\n", 63 | "model = RandomForestClassifier(n_estimators=100,random_state=0) # ランダムフォレストのインスタンスを作成\n", 64 | "\n", 65 | "model.fit(features,target) # 学習の実行\n", 66 | "\n", 67 | "# 予測\n", 68 | "pred = model.predict(test_X)\n", 69 | "\n", 70 | "# 予測精度の確認\n", 71 | "from sklearn.metrics import accuracy_score\n", 72 | "\n", 73 | "print(accuracy_score(pred,test_y))\n", 74 | "\n", 75 | "# 重要度の表示\n", 76 | "importance = model.feature_importances_ \n", 77 | "\n", 78 | "print('Feature Importances:')\n", 79 | "for i, feat in enumerate(['pclass','age','sex','fare','embarked']):\n", 80 | " print('\\t{0:20s} : {1:>.5f}'.format(feat, importance[i]))\n", 81 | " \n", 82 | "# csvで出力\n", 83 | "df_pred = pd.DataFrame(pred)\n", 84 | "df_pred.to_csv('../output/submission.csv',header=None)" 85 | ] 86 | } 87 | ], 88 | "metadata": { 89 | "kernelspec": { 90 | "display_name": "Python 3", 91 | "language": "python", 92 | "name": "python3" 93 | }, 94 | "language_info": { 95 | "codemirror_mode": { 96 | "name": "ipython", 97 | "version": 3 98 | }, 99 | "file_extension": ".py", 100 | "mimetype": "text/x-python", 101 | "name": "python", 102 | "nbconvert_exporter": "python", 103 | "pygments_lexer": "ipython3", 104 | "version": "3.7.6" 105 | } 106 | }, 107 | "nbformat": 4, 108 | "nbformat_minor": 4 109 | } 110 | -------------------------------------------------------------------------------- /output/submission.csv: -------------------------------------------------------------------------------- 1 | 0,0 2 | 1,1 3 | 2,0 4 | 3,0 5 | 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