どーも Keita_Nakamori(´・ω・`)です。pandas の基本操作を一通りやりましたので、置いておきます。
pandas_for_text_of _data_science.html
pandas_for_text_of _data_science.ipynb
べた貼りです。上記のhtmlのほうが見やすいです。
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In [2]: import pandas as pd In [3]: # Seriesを作成する li = [1,2,3,4,5] ser = pd.Series(li) ser Out[3]: 0 1 1 2 2 3 3 4 4 5 dtype: int64 In [4]: # DataFrameを作成する li = [['aaa', 1, True], ['bbb', 2, False], ['ccc', 3, False]] df = pd.DataFrame(li) df # 一つの列に文字列が混在すると演算できなくなるので注意 Out[4]: 0 1 2 0 aaa 1 True 1 bbb 2 False 2 ccc 3 False In [5]: # DataFrameを観察する import numpy as np import pandas as pd df =pd.DataFrame(np.arange(100).reshape(25,4)) df.head(3) #データの頭の3行を表示する df.tail(3) #データの尾の3行を表示する df.shape #(25, 4) 25行 x 4列 Out[5]: (25, 4) In [6]: # DataFrameのindex とcolumns を定義する df = pd.DataFrame(np.arange(6).reshape(3,2)) df.index = ['001', '002', '003'] df.columns =['Value_1', 'Value_2'] df Out[6]: Value_1 Value_2 001 0 1 002 2 3 003 4 5 In [7]: # index指定 df[0:2] Out[7]: Value_1 Value_2 001 0 1 002 2 3 In [8]: # columns指定 df['Value_1'] Out[8]: 001 0 002 2 003 4 Name: Value_1, dtype: int32 In [9]: # columns指定 複数の場合はリストで渡す df[['Value_1','Value_2']] Out[9]: Value_1 Value_2 001 0 1 002 2 3 003 4 5 In [10]: # ディクショナリから DataFrameを作成する dic ={'aaa':[1,2,3,4], 'bbb':[5,6,7,8], 'c':[9,10,11,12]} df = pd.DataFrame(dic) df Out[10]: aaa bbb c 0 1 5 9 1 2 6 10 2 3 7 11 3 4 8 12 In [11]: # ロケーション指定 loc , iloc import numpy as np import pandas as pd # 24行x 4列のDataFrameを生成 df = pd.DataFrame(np.arange(5*4).reshape(5,4)) df.index = ['row1', 'row2', 'row3', 'row4', 'row5'] df.columns =['A', 'B', 'C', 'D', ] df Out[11]: A B C D row1 0 1 2 3 row2 4 5 6 7 row3 8 9 10 11 row4 12 13 14 15 row5 16 17 18 19 In [12]: df.loc[:,:] # 必ず index columnsの両方を指定するので曖昧さがない df.loc[:,'B'] Out[12]: row1 1 row2 5 row3 9 row4 13 row5 17 Name: B, dtype: int32 In [13]: df.loc['row3','B'] Out[13]: 9 In [14]: df.loc[['row1', 'row2'], 'B'] Out[14]: row1 1 row2 5 Name: B, dtype: int32 In [15]: df.iloc[0,0] # 0 df.iloc[2:4, :] Out[15]: A B C D row3 8 9 10 11 row4 12 13 14 15 In [16]: # データの読み込み df_csv = pd.read_csv('data/sample_data.csv', encoding='utf-8', index_col=0, parse_dates=True) df_excel = pd.read_excel('data/sample_data.xlsx', encoding='utf-8', index_col=0, parse_dates=True) df = df_csv In [17]: # htmlのtableから表を抜き出す # url = "" # tables = pd.read_html(url) # df_html = tables[0] In [18]: # データの書き込み df.to_csv('data/write_data.csv') # csvデータ df.to_excel('data/write_data.xlsx') # エクセルデータ df.to_pickle('data/write_data.pickle') # ピクルデータ In [19]: # 保存したデータの読み込み df_csv = pd.read_csv('data/sample_data.csv', encoding='utf-8') df_pickle = pd.read_pickle('data/write_data.pickle') In [20]: df_pickle Out[20]: column_1 column_2 column_3 column_4 column_5 column_6 rows row1 1 1.01 aaa True aaa aaa row2 2 2.01 bbb False bbb bbb row3 3 3.01 aaa True NaN 4.01 row4 4 4.01 bbb False NaN 5.01 row5 5 5.01 aaa True NaN 7 row6 6 6.01 bbb False bbb 8 row7 7 7.01 aaa True aaa NaN row8 8 8.01 bbb False bbb NaN row9 9 9.01 aaa True aaa FALSE row10 10 10.01 bbb False bbb TRUE In [21]: # 条件抽出 df_bool = df_pickle['column_2'] >= 5 df_bool Out[21]: rows row1 False row2 False row3 False row4 False row5 True row6 True row7 True row8 True row9 True row10 True Name: column_2, dtype: bool In [22]: df_selected = df_pickle[df_pickle['column_2'] > 5] df_selected Out[22]: column_1 column_2 column_3 column_4 column_5 column_6 rows row5 5 5.01 aaa True NaN 7 row6 6 6.01 bbb False bbb 8 row7 7 7.01 aaa True aaa NaN row8 8 8.01 bbb False bbb NaN row9 9 9.01 aaa True aaa FALSE row10 10 10.01 bbb False bbb TRUE In [23]: # 条件抽出 SQLのようにqueryメソドを使用する df_pickle.query('column_2 >= 5 and column_2 <= 9') Out[23]: column_1 column_2 column_3 column_4 column_5 column_6 rows row5 5 5.01 aaa True NaN 7 row6 6 6.01 bbb False bbb 8 row7 7 7.01 aaa True aaa NaN row8 8 8.01 bbb False bbb NaN In [24]: # 各列の型を確認する df.dtypes df_pickle.dtypes Out[24]: column_1 int64 column_2 float64 column_3 object column_4 bool column_5 object column_6 object dtype: object In [25]: # 型を変更する df = df_pickle # 以後説明しやすいように名前を変更しておきます。 df.loc[:,'date'] = df.loc[:,'column_1'] df['date'] = df['date'].apply(pd.to_datetime) df Out[25]: column_1 column_2 column_3 column_4 column_5 column_6 date rows row1 1 1.01 aaa True aaa aaa 1970-01-01 00:00:00.000000001 row2 2 2.01 bbb False bbb bbb 1970-01-01 00:00:00.000000002 row3 3 3.01 aaa True NaN 4.01 1970-01-01 00:00:00.000000003 row4 4 4.01 bbb False NaN 5.01 1970-01-01 00:00:00.000000004 row5 5 5.01 aaa True NaN 7 1970-01-01 00:00:00.000000005 row6 6 6.01 bbb False bbb 8 1970-01-01 00:00:00.000000006 row7 7 7.01 aaa True aaa NaN 1970-01-01 00:00:00.000000007 row8 8 8.01 bbb False bbb NaN 1970-01-01 00:00:00.000000008 row9 9 9.01 aaa True aaa FALSE 1970-01-01 00:00:00.000000009 row10 10 10.01 bbb False bbb TRUE 1970-01-01 00:00:00.000000010 In [26]: # int から floatに型を変更する df['float'] = df['column_1'].astype(np.float32) df Out[26]: column_1 column_2 column_3 column_4 column_5 column_6 date float rows row1 1 1.01 aaa True aaa aaa 1970-01-01 00:00:00.000000001 1.0 row2 2 2.01 bbb False bbb bbb 1970-01-01 00:00:00.000000002 2.0 row3 3 3.01 aaa True NaN 4.01 1970-01-01 00:00:00.000000003 3.0 row4 4 4.01 bbb False NaN 5.01 1970-01-01 00:00:00.000000004 4.0 row5 5 5.01 aaa True NaN 7 1970-01-01 00:00:00.000000005 5.0 row6 6 6.01 bbb False bbb 8 1970-01-01 00:00:00.000000006 6.0 row7 7 7.01 aaa True aaa NaN 1970-01-01 00:00:00.000000007 7.0 row8 8 8.01 bbb False bbb NaN 1970-01-01 00:00:00.000000008 8.0 row9 9 9.01 aaa True aaa FALSE 1970-01-01 00:00:00.000000009 9.0 row10 10 10.01 bbb False bbb TRUE 1970-01-01 00:00:00.000000010 10.0 In [27]: # 並べ替え .sort() df.sort_values(by = 'column_2', ascending=True) # 昇順 df.sort_values(by = 'column_2', ascending=False) # 降順 Out[27]: column_1 column_2 column_3 column_4 column_5 column_6 date float rows row10 10 10.01 bbb False bbb TRUE 1970-01-01 00:00:00.000000010 10.0 row9 9 9.01 aaa True aaa FALSE 1970-01-01 00:00:00.000000009 9.0 row8 8 8.01 bbb False bbb NaN 1970-01-01 00:00:00.000000008 8.0 row7 7 7.01 aaa True aaa NaN 1970-01-01 00:00:00.000000007 7.0 row6 6 6.01 bbb False bbb 8 1970-01-01 00:00:00.000000006 6.0 row5 5 5.01 aaa True NaN 7 1970-01-01 00:00:00.000000005 5.0 row4 4 4.01 bbb False NaN 5.01 1970-01-01 00:00:00.000000004 4.0 row3 3 3.01 aaa True NaN 4.01 1970-01-01 00:00:00.000000003 3.0 row2 2 2.01 bbb False bbb bbb 1970-01-01 00:00:00.000000002 2.0 row1 1 1.01 aaa True aaa aaa 1970-01-01 00:00:00.000000001 1.0 In [28]: # 関数を使って条件分岐する def judge(value): if value >= 7: return 'good' if value >=4: return 'nomal' else: return 'bad' df['judged'] = df['column_2'].apply(judge) df Out[28]: column_1 column_2 column_3 column_4 column_5 column_6 date float judged rows row1 1 1.01 aaa True aaa aaa 1970-01-01 00:00:00.000000001 1.0 bad row2 2 2.01 bbb False bbb bbb 1970-01-01 00:00:00.000000002 2.0 bad row3 3 3.01 aaa True NaN 4.01 1970-01-01 00:00:00.000000003 3.0 bad row4 4 4.01 bbb False NaN 5.01 1970-01-01 00:00:00.000000004 4.0 nomal row5 5 5.01 aaa True NaN 7 1970-01-01 00:00:00.000000005 5.0 nomal row6 6 6.01 bbb False bbb 8 1970-01-01 00:00:00.000000006 6.0 nomal row7 7 7.01 aaa True aaa NaN 1970-01-01 00:00:00.000000007 7.0 good row8 8 8.01 bbb False bbb NaN 1970-01-01 00:00:00.000000008 8.0 good row9 9 9.01 aaa True aaa FALSE 1970-01-01 00:00:00.000000009 9.0 good row10 10 10.01 bbb False bbb TRUE 1970-01-01 00:00:00.000000010 10.0 good In [29]: # One-Hot エンコーディングをする df_moved = pd.get_dummies(df['judged'], prefix='J') df_moved Out[29]: J_bad J_good J_nomal rows row1 1 0 0 row2 1 0 0 row3 1 0 0 row4 0 0 1 row5 0 0 1 row6 0 0 1 row7 0 1 0 row8 0 1 0 row9 0 1 0 row10 0 1 0 In [30]: # pickle保存しておく pd.to_pickle(df_moved, 'data/df_moved.pickle') In [31]: # 時系列データの作成 dates = pd.date_range(start='2019-01-01', end='2019-1-30') np.random.seed(0) df =pd.DataFrame(np.random.randint(1,31,30), index=dates, columns=['rand_value']) df.head() Out[31]: rand_value 2019-01-01 13 2019-01-02 16 2019-01-03 22 2019-01-04 1 2019-01-05 4 In [32]: # 時系列データの作成２ dates = pd.date_range(start='2019-01-01', periods=365) np.random.seed(1) df = pd.DataFrame(np.random.randint(1, 31, 365), index=dates, columns=['rand_value']) df.head() Out[32]: rand_value 2019-01-01 6 2019-01-02 12 2019-01-03 13 2019-01-04 9 2019-01-05 10 In [33]: # 月平均を算出する .grooupby(pd.Grouper()) df.groupby(pd.Grouper(freq='M')).mean().head() Out[33]: rand_value 2019-01-31 14.870968 2019-02-28 15.000000 2019-03-31 15.387097 2019-04-30 15.700000 2019-05-31 14.193548 In [34]: # 月平均を算出する２ .resample() df['rand_value'].resample('M').mean().head() # Serieseで返ってくる Out[34]: 2019-01-31 14.870968 2019-02-28 15.000000 2019-03-31 15.387097 2019-04-30 15.700000 2019-05-31 14.193548 Freq: M, Name: rand_value, dtype: float64 In [35]: # 年間の土曜日の日付データを作成する pd.date_range(start='2019-01-01', end='2019-12-31', freq='W-SAT') Out[35]: DatetimeIndex(['2019-01-05', '2019-01-12', '2019-01-19', '2019-01-26', '2019-02-02', '2019-02-09', '2019-02-16', '2019-02-23', '2019-03-02', '2019-03-09', '2019-03-16', '2019-03-23', '2019-03-30', '2019-04-06', '2019-04-13', '2019-04-20', '2019-04-27', '2019-05-04', '2019-05-11', '2019-05-18', '2019-05-25', '2019-06-01', '2019-06-08', '2019-06-15', '2019-06-22', '2019-06-29', '2019-07-06', '2019-07-13', '2019-07-20', '2019-07-27', '2019-08-03', '2019-08-10', '2019-08-17', '2019-08-24', '2019-08-31', '2019-09-07', '2019-09-14', '2019-09-21', '2019-09-28', '2019-10-05', '2019-10-12', '2019-10-19', '2019-10-26', '2019-11-02', '2019-11-09', '2019-11-16', '2019-11-23', '2019-11-30', '2019-12-07', '2019-12-14', '2019-12-21', '2019-12-28'], dtype='datetime64[ns]', freq='W-SAT') In [36]: # 土曜日までの１週間単位でデータをまとめる df_year = pd.DataFrame(df.groupby(pd.Grouper(freq='W-SAT')).sum(), columns=['rand_value']) df_year.head() Out[36]: rand_value 2019-01-05 50 2019-01-12 67 2019-01-19 124 2019-01-26 127 2019-02-02 127 In [37]: #欠損値の処理 df_nan = pd.read_pickle('data/write_data.pickle') #欠損値の行を削除する df_droped = df_nan.dropna() df_droped Out[37]: column_1 column_2 column_3 column_4 column_5 column_6 rows row1 1 1.01 aaa True aaa aaa row2 2 2.01 bbb False bbb bbb row6 6 6.01 bbb False bbb 8 row9 9 9.01 aaa True aaa FALSE row10 10 10.01 bbb False bbb TRUE In [38]: #欠損値に0を代入して埋める df_fillna = df_nan.fillna(0) df_fillna Out[38]: column_1 column_2 column_3 column_4 column_5 column_6 rows row1 1 1.01 aaa True aaa aaa row2 2 2.01 bbb False bbb bbb row3 3 3.01 aaa True 0 4.01 row4 4 4.01 bbb False 0 5.01 row5 5 5.01 aaa True 0 7 row6 6 6.01 bbb False bbb 8 row7 7 7.01 aaa True aaa 0 row8 8 8.01 bbb False bbb 0 row9 9 9.01 aaa True aaa FALSE row10 10 10.01 bbb False bbb TRUE In [39]: #欠損値を１つ手前の値で補完する df_fill = df_nan.fillna(method='ffill') df_fill Out[39]: column_1 column_2 column_3 column_4 column_5 column_6 rows row1 1 1.01 aaa True aaa aaa row2 2 2.01 bbb False bbb bbb row3 3 3.01 aaa True bbb 4.01 row4 4 4.01 bbb False bbb 5.01 row5 5 5.01 aaa True bbb 7 row6 6 6.01 bbb False bbb 8 row7 7 7.01 aaa True aaa 8 row8 8 8.01 bbb False bbb 8 row9 9 9.01 aaa True aaa FALSE row10 10 10.01 bbb False bbb TRUE In [40]: # データの連結 df = pd.read_pickle('data/df_sample.pickle') df_moved = pd.read_pickle('data/df_moved.pickle') # 列方向の連結 df_merged = pd.concat([df, df_moved], axis=1) df_merged.head() C:\Users\keita\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\api.py:107: RuntimeWarning: '<' not supported between instances of 'float' and 'str', sort order is undefined for incomparable objects result = result.union(other) Out[40]: column_2 column_3 column_4 column_5 column_6 c2*c4 jadged judged J_bad J_good J_nomal 1.0 1.01 aaa True aaa aaa 1.01 bad bad NaN NaN NaN 2.0 2.01 bbb False bbb bbb 0.00 bad bad NaN NaN NaN 3.0 3.01 aaa True NaN 4.01 3.01 bad bad NaN NaN NaN 4.0 4.01 bbb False NaN 5.01 0.00 nomal nomal NaN NaN NaN 5.0 5.01 aaa True NaN 7 5.01 nomal nomal NaN NaN NaN In [41]: # 行方向に連結する df_1 = df.loc[1:2,:] df_2 = df.loc[9:10,:] df = pd.concat([df_1, df_2], axis=0, sort=True) df Out[41]: c2*c4 column_2 column_3 column_4 column_5 column_6 jadged judged float 1.0 1.01 1.01 aaa True aaa aaa bad bad 2.0 0.00 2.01 bbb False bbb bbb bad bad 9.0 9.01 9.01 aaa True aaa FALSE good good 10.0 0.00 10.01 bbb False bbb TRUE good good In [42]: # 基本的な統計量を算出する np.random.seed(2) df = pd.DataFrame(np.random.randint(0,11,100), columns=['value']) print(df.loc[:,'value'].max(), df.loc[:,'value'].min(), df.loc[:,'value'].mode(), #最頻値 df.loc[:,'value'].mean(), df.loc[:,'value'].median(), df.loc[:,'value'].std(ddof=0), # 母集団の標準偏差 df.loc[:,'value'].std(ddof=1), # サンプルの標準偏差 ) 10 0 0 10 dtype: int32 5.55 6.0 3.1124748994971836 3.128154973039785 In [43]: #カウント df[df.loc[:,'value'] == 5].count() Out[43]: value 9 dtype: int64 In [44]: # 要約された統計量を算出する df.describe() Out[44]: value count 100.000000 mean 5.550000 std 3.128155 min 0.000000 25% 3.000000 50% 6.000000 75% 8.000000 max 10.000000 In [45]: # 相関係数 data = np.random.randn(300).reshape(100,3) df = pd.DataFrame(data) df.head() df.corr() from pandas.plotting import scatter_matrix %matplotlib inline _ = scatter_matrix(df) In [46]: # pandas.DataFrame から numpy.arrayへの変換 df[0].values Out[46]: array([ 1.02210506e+00, 1.53636029e+00, -1.18854160e+00, -3.18840605e-01, 1.95254214e-01, 1.20769783e+00, 4.17160217e-01, 1.20769557e-01, 2.35854982e-01, 1.47630693e+00, -2.57855698e+00, 2.23092726e-01, 5.48677242e-01, 1.29975337e-01, 1.33354447e-01, -1.63213498e+00, 1.16837169e+00, 4.08842472e-02, -3.95195402e-01, -1.17113099e+00, 5.57721015e-01, 2.10202473e-02, 1.96504656e-01, 6.38925860e-01, -3.77376397e-02, 9.26374048e-03, -4.32477714e-02, 8.19477959e-01, -2.74608267e-01, -1.11840069e+00, 1.02868791e+00, -9.17827897e-02, 1.00139308e+00, 1.36927887e+00, -3.32376106e-01, -8.98514459e-01, 6.60488981e-01, 1.30103340e+00, 5.00463181e-01, -3.79377283e-01, -5.18945829e-01, 9.09490540e-01, -1.08860575e+00, -2.81766922e-01, -8.00976861e-01, 1.37357822e+00, -1.28768987e+00, -9.37719104e-01, -7.47252631e-01, -5.76454194e-01, -1.41699281e+00, -1.36057075e-01, -8.81027234e-01, -1.77330774e+00, 3.31707535e-02, -1.04990183e-01, -1.71142690e+00, -9.12673226e-01, -1.08172822e+00, 1.99335230e+00, -3.86961106e-01, -2.09225941e-01, -4.27889084e-01, 6.06603798e-01, -3.36370275e-01, 1.80052517e+00, 8.33292653e-01, 1.33480770e+00, -1.44644975e+00, 6.66240193e-01, -1.64386955e+00, 6.11191152e-01, 4.77865842e-01, 9.18109263e-01, -7.38847589e-01, 1.57718123e+00, 8.67298665e-01, -2.42036059e-01, -3.21914568e-01, 1.01525122e+00, -5.69088895e-01, 8.48138843e-01, -2.13080187e-02, 1.73029626e+00, 3.23823588e-01, 1.20917022e+00, -8.33636664e-01, -1.29003470e+00, -3.13110100e-01, 3.57341451e-01, -4.10464725e-01, -1.84265262e-03, -6.61755432e-01, -6.22129678e-01, -8.87283517e-01, 1.16993295e+00, 3.32767248e-01, -4.41954269e-01, 4.31653084e-01, -1.20600785e+00]) In [ ]: # 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