--- disqus: HackMD --- # 10分鐘的Pandas入門-繁中版 ###### tags: `Pandas` 本篇網址:https://hackmd.io/@wiimax/10-minutes-to-pandas >來自Pandas官方文件,原文詳見: https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html > ## Pandas介紹 - 此份介紹源自官方文件,是對Pandas的簡短介紹,~~其實一點也不短~~。 - 需要使用的模組 ```python import numpy as np import pandas as pd ``` - 後續繪圖會使用的模組 ```python import matplotlib.pyplot as plt ``` ## Object creation 創建物件 - 參閱官方文件[Data Structure Intro section](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dsintro) - 通過傳入一個list創建`Series`,pandas預設會產生整數的index - View in [Colab]( https://colab.research.google.com/drive/17PUt1FOo1yCkeWJO0NiQj8Et3eOlGxRU?authuser=1#scrollTo=1NLd9wAHBjnB) ```python s = pd.Series([1, 3, 5, np.nan, 6, 8]) s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 ``` - 以帶有日期的datatime index及標籤欄位創建`DataFrame` ```python dates = pd.date_range('20130101', periods=6) dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') ``` ```python df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) df Out[8]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 ``` - 以字典`dict`創建`DataFrame` ```python df2 = pd.DataFrame({'A': 1., 'B': pd.Timestamp('20130102'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo'}) df2 Out[10]: A B C D E F 0 1.0 2013-01-02 1.0 3 test foo 1 1.0 2013-01-02 1.0 3 train foo 2 1.0 2013-01-02 1.0 3 test foo 3 1.0 2013-01-02 1.0 3 train foo The columns of the resulting DataFrame have different dtypes. ``` ```python In [11]: df2.dtypes Out[11]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object ``` - 如果用IPython、Jupyter notebook等筆記本形式使用`Tab`可自動展示補全所有的屬性、自定義欄位。 ```python In [12]: df2.<TAB> df2.A df2.bool df2.abs df2.boxplot df2.add df2.C df2.add_prefix df2.clip df2.add_suffix df2.clip_lower df2.align df2.clip_upper df2.all df2.columns df2.any df2.combine df2.append df2.combine_first df2.apply df2.compound df2.applymap df2.consolidate df2.D ``` ## Viewing data 檢視資料 - 參閱[Basics section](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics) - 以`.head(n)`查前n筆資料、以`tail(n)`查看末n筆資料。 ```python In [13]: df.head() Out[13]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 ``` ```python In [14]: df.tail(3) Out[14]: A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 ``` - 以`.index`, `.columns`顯示索引及欄位名稱。 ```python In [15]: df.index Out[15]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') ``` ```python In [16]: df.columns Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object') ``` - `DataFrame.to_numpy()`轉換為`NumPy`,但請注意如果該`DataFrame`具有不同資料型態(int、str...),這可能是一項昂貴的操作,主因是NumPy數組對整個數組有一個dtype,而pandas DataFrames每列有一個dtype。當呼叫時 DataFrame.to_numpy(),pandas會找到可以容納 DataFrame中所有 dtypes 的NumPy dtype。這可能最終成為object,這需要將每個值都轉換為Python物件。 - 以下df的DataFrame值皆為浮點數,` DataFrame.to_numpy()`就相當快。 ```python In [17]: df.to_numpy() Out[17]: array([[ 0.4691, -0.2829, -1.5091, -1.1356], [ 1.2121, -0.1732, 0.1192, -1.0442], [-0.8618, -2.1046, -0.4949, 1.0718], [ 0.7216, -0.7068, -1.0396, 0.2719], [-0.425 , 0.567 , 0.2762, -1.0874], [-0.6737, 0.1136, -1.4784, 0.525 ]]) ``` - 以下df2的`DataFrame`有不同`dtypes`,運算代價高 ```python In [18]: df2.to_numpy() Out[18]: array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object) ``` >Note DataFrame.to_numpy() does not include the index or column labels in the output. - 以`describe()`快速檢視數據統計摘要 ```python In [19]: df.describe() Out[19]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1.368714 -1.076610 50% 0.022070 -0.228039 -0.767252 -0.386188 75% 0.658444 0.041933 -0.034326 0.461706 max 1.212112 0.567020 0.276232 1.071804 ``` - 以`T`轉置資料矩陣(列、欄互換) ```python In [20]: df.T Out[20]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690 B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648 C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988 ``` - 依軸排序`sort_index(axis=1, ascending=False)`,結果為以ROW、遞增排序。 ```python In [21]: df.sort_index(axis=1, ascending=False) Out[21]: D C B A 2013-01-01 -1.135632 -1.509059 -0.282863 0.469112 2013-01-02 -1.044236 0.119209 -0.173215 1.212112 2013-01-03 1.071804 -0.494929 -2.104569 -0.861849 2013-01-04 0.271860 -1.039575 -0.706771 0.721555 2013-01-05 -1.087401 0.276232 0.567020 -0.424972 2013-01-06 0.524988 -1.478427 0.113648 -0.673690 ``` - 以值排序 ```python In [22]: df.sort_values(by='B') Out[22]: A B C D 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 ``` ## Selection 選取 >注意,雖然標準的`Python`、`numpy`表達式直觀可用,但建議以`Pandas`優化的選擇方法,如`.at`、`.iat`、` .loc`和`.iloc`。 參閱indexing documentation [Indexing and Selecting Data](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing) and [MultiIndex / Advanced Indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced). ### Getting 取得資料 - 選取單一欄位,將會回傳一個`Series`, `df['A']`相當於`df.A`: ```python In [23]: df['A'] Out[23]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64 ``` - 以中括號`[]`選擇想要的rows進行切片 ```python In [24]: df[0:3] Out[24]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 ``` ```python In [25]: df['20130102':'20130104'] Out[25]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 ``` ### 以標籤進行選擇 參閱[Selection by Label](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-label)了解更多 - 使用標籤取得交叉區域: ```python #即取得2013-01-01的數據 In [26]: df.loc[dates[0]] Out[26]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64 ``` - 以標籤取得多欄位數據 ```python In [27]: df.loc[:, ['A', 'B']] Out[27]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648 ``` - 以標籤組合切片: ```python In [28]: df.loc['20130102':'20130104', ['A', 'B']] Out[28]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 ``` - 以標籤組合縮減顯示維度: ```python In [29]: df.loc['20130102', ['A', 'B']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64 ``` - 獲取單筆數值: ```python In [30]: df.loc[dates[0], 'A'] Out[30]: 0.4691122999071863 In [31]: df.at[dates[0], 'A'] Out[31]: 0.4691122999071863 ``` > `loc`以標籤取得Rows數據,`iloc`以行號取得數據。 ### Selection by position 以位置選擇 - 在[Selection by Position](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-integer)查看更多 - 以整數數值選擇: ```python In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64 ``` - 以整數切片,使用方式類似`numpy`、`python`風格: ```python In [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 20` - 以list指定位置,使用方式類似`numpy`、`python`風格: ```python In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232 ``` - 對行rows切片: ```python In [35]: df.iloc[1:3, :] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 ``` - 對欄columns切片: ```python In [36]: df.iloc[:, 1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427 ``` - 取得特定值: ```python In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858 In [38]: df.iat[1, 1] Out[38]: -0.17321464905330858 ``` ### Boolean indexing 布林索引 - 以單欄的值選取數據 ```python In [39]: df[df.A > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 ``` - 以`where`條件判斷選擇數據 ```python In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988 ``` - 以`isin()`方法篩選數據: ```python In [41]: df2 = df.copy() In [42]: df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three'] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2['E'].isin(['two', 'four'])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four ``` ### Setting 設置 - 設置新欄位column將自動以index對齊資料 ```python In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df['F'] = s1 ``` - 以標籤更新數值: ```python In [48]: df.at[dates[0], 'A'] = 0 ``` - 以位置更新數值: ```python In [49]: df.iat[0, 1] = 0 ``` - 以NumPy array更新 ```python In [50]: df.loc[:, 'D'] = np.array([5] * len(df)) ``` - df依前述操作更新結果 ```python In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 2013-01-05 -0.424972 0.567020 0.276232 5 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5 5.0 ``` - 以`where`條件判斷運算子更新值 ```python In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0 ``` ## Missing data 缺失值處裡 - `pandas`以`np.nan`表示缺失值,預設情況不進行運算,參閱 [Missing Data section](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#missing-data) - `.reindex()`可以修改/增加/刪除索引,將回傳一個數據的副本 ```python In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E']) In [56]: df1.loc[dates[0]:dates[1], 'E'] = 1 In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN ``` - 丟掉有區失值的行 ```python In [58]: df1.dropna(how='any') Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 ``` - 對缺失值賦值 ```python In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0 ``` - 以`.isna()`使用布林遮罩 ```python In [60]: pd.isna(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True ``` ## Operations 操作 - 參閱[Basic section on Binary Ops](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-binop)。 ### Stats 統計 - 操作通常不包含缺失項(缺失要先預處理) - 執行敘述統計-按列 ```python In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64 ``` - 執行敘述統計-按欄 ```python In [62]: df.mean(1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64 ``` - 如要操作不同維度需先對齊,Pandas會自動沿著指定維度廣播(broadcasting) ```python #以時間為index對齊 #.shift(2)為資料沿軸順移2位 In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis='index') Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN ``` ### Apply 應用 - 以Applying functions進行資料處理: ```python In [66]: df.apply(np.cumsum) #累加 Out[66]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 -1.389850 10 1.0 2013-01-03 0.350263 -2.277784 -1.884779 15 3.0 2013-01-04 1.071818 -2.984555 -2.924354 20 6.0 2013-01-05 0.646846 -2.417535 -2.648122 25 10.0 2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0 In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A 2.073961 B 2.671590 C 1.785291 D 0.000000 F 4.000000 dtype: float64 ``` ### Histogramming 直方圖 - 至[Histogramming and Discretization](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-discretization)了解更多. ```python In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 6 2 2 2 1 1 dtype: int64 ``` ### String Methods 字串處理方法 - `Pandas.Series`在`.str`屬性中配備了一組字符串處理方法,可以輕鬆地對數組的每個元素進行操作,如下面的代碼片段所示。請注意,str中的模式匹配通常默認使用[正則表達式](https://docs.python.org/3/library/re.html)(在某些情況下總是使用它們)。在[Vectorized String Methods](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#text-string-methods)中查看更多信息。 ```python In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object ``` ## Merge合併 ### Concat連接 - pandas提供各種簡易的合併Series及Dataframe物件操作方式,參閱[Merging section](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging) - 以`concat()`連接pandas物件: ```python In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] #分段 In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 ``` ### Join - 可以採用SQL style合併。參閱[Database style joining](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging-join)章節。 ```python In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]}) In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]}) In [79]: left Out[79]: key lval 0 foo 1 1 foo 2 In [80]: right Out[80]: key rval 0 foo 4 1 foo 5 In [81]: pd.merge(left, right, on='key') Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5 ``` - 另一個例子: ```python In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]}) In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]}) In [84]: left Out[84]: key lval 0 foo 1 1 bar 2 In [85]: right Out[85]: key rval 0 foo 4 1 bar 5 In [86]: pd.merge(left, right, on='key') Out[86]: key lval rval 0 foo 1 4 1 bar 2 5 ``` ### Append 附加 - 將行附加到dataframe,參見[Appending](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging-concatenation)章節 ```python In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) In [88]: df Out[88]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758 In [89]: s = df.iloc[3] In [90]: df.append(s, ignore_index=True) Out[90]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758 8 1.453749 1.208843 -0.080952 -0.264610 #append this row ``` ## Grouping - 透過“group by”將數據對每個分組應用不同的function並結合展示成果,過程為: - 依據某種標準將數據拆分(Splitting)為組 - 將設計好的功能(applying)對每個組獨立處理。 - 結合(Combining)成果至資料結構 - 參閱[Grouping](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#groupby)章節. - ```python In [91]: pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], 'C': np.random.randn(8), 'D': np.random.randn(8)}) In [92]: df Out[92]: A B C D 0 foo one -1.202872 -0.055224 1 bar one -1.814470 2.395985 2 foo two 1.018601 1.552825 3 bar three -0.595447 0.166599 4 foo two 1.395433 0.047609 5 bar two -0.392670 -0.136473 6 foo one 0.007207 -0.561757 7 foo three 1.928123 -1.623033 ``` - 分組然後將`sum()`應用於結果 ```python In [93]: df.groupby('A').sum() Out[93]: C D A bar -2.802588 2.42611 foo 3.146492 -0.63958 ``` - 以多欄位分組形成分層索引,並應用`sum()` ```python In [94]: df.groupby(['A', 'B']).sum() Out[94]: C D A B bar one -1.814470 2.395985 three -0.595447 0.166599 two -0.392670 -0.136473 foo one -1.195665 -0.616981 three 1.928123 -1.623033 two 2.414034 1.600434 ``` ## Reshaping 重塑 參閱[Hierarchical Indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-hierarchical) and [Reshaping](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html#reshaping-stacking)章節內容 ### Stack 堆疊 ```python In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])) #pandas.MultiIndex.from_tuples將包含多個list的元組轉換為複雜索引 In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B']) In [98]: df2 = df[:4] In [99]: df2 Out[99]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230 ``` - 使用`stack()`方法將DataFrame壓縮(compresses) 為階層形式的欄位 ```python In [100]: stacked = df2.stack() In [101]: stacked Out[101]: first second bar one A 0.029399 B -0.542108 two A 0.282696 B -0.087302 baz one A -1.575170 B 1.771208 two A 0.816482 B 1.100230 dtype: float64 ``` - 使用堆疊的DataFrame或Series(具有階層索引),與`stack()`相反的操作為`unstack()`,預設情況下為取消堆疊最後一級: ```python In [102]: stacked.unstack() Out[102]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230 In [103]: stacked.unstack(1) Out[103]: second one two first bar A 0.029399 0.282696 B -0.542108 -0.087302 baz A -1.575170 0.816482 B 1.771208 1.100230 In [104]: stacked.unstack(0) Out[104]: first bar baz second one A 0.029399 -1.575170 B -0.542108 1.771208 two A 0.282696 0.816482 B -0.087302 1.100230 ``` ### Pivot tables - 參閱[Pivot Tables](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html#reshaping-pivot) ```python In [105]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3, 'B': ['A', 'B', 'C'] * 4, 'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2, 'D': np.random.randn(12), 'E': np.random.randn(12)}) In [106]: df Out[106]: A B C D E 0 one A foo 1.418757 -0.179666 1 one B foo -1.879024 1.291836 2 two C foo 0.536826 -0.009614 3 three A bar 1.006160 0.392149 4 one B bar -0.029716 0.264599 5 one C bar -1.146178 -0.057409 6 two A foo 0.100900 -1.425638 7 three B foo -1.035018 1.024098 8 one C foo 0.314665 -0.106062 9 one A bar -0.773723 1.824375 10 two B bar -1.170653 0.595974 11 three C bar 0.648740 1.167115 ``` - 我們可以非常輕鬆地從這些數據生成數據透視表: ```python In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C']) Out[107]: C bar foo A B one A -0.773723 1.418757 B -0.029716 -1.879024 C -1.146178 0.314665 three A 1.006160 NaN B NaN -1.035018 C 0.648740 NaN two A NaN 0.100900 B -1.170653 NaN C NaN 0.536826 ``` ## Time series 時間序列 - pandas具有簡單,強大且高效的功能,用於在頻率轉換期間執行重採樣操作(例如,將第二數據轉換為5分鐘數據)。這在財務應用程序中非常常見,但不僅限於此。請參閱[Time Series](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries)章節 ```python In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S') In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [110]: ts.resample('5Min').sum() Out[110]: 2012-01-01 25083 Freq: 5T, dtype: int64 ``` - 時區呈現: ```python In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D') In [112]: ts = pd.Series(np.random.randn(len(rng)), rng) In [113]: ts Out[113]: 2012-03-06 0.464000 2012-03-07 0.227371 2012-03-08 -0.496922 2012-03-09 0.306389 2012-03-10 -2.290613 Freq: D, dtype: float64 In [114]: ts_utc = ts.tz_localize('UTC') In [115]: ts_utc Out[115]: 2012-03-06 00:00:00+00:00 0.464000 2012-03-07 00:00:00+00:00 0.227371 2012-03-08 00:00:00+00:00 -0.496922 2012-03-09 00:00:00+00:00 0.306389 2012-03-10 00:00:00+00:00 -2.290613 Freq: D, dtype: float64 ``` - 轉換為另一個時區: ```python In [116]: ts_utc.tz_convert('US/Eastern') Out[116]: 2012-03-05 19:00:00-05:00 0.464000 2012-03-06 19:00:00-05:00 0.227371 2012-03-07 19:00:00-05:00 -0.496922 2012-03-08 19:00:00-05:00 0.306389 2012-03-09 19:00:00-05:00 -2.290613 Freq: D, dtype: float64 ``` - 在不同時間跨度表示間轉換: ```python In [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M') In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [119]: ts Out[119]: 2012-01-31 -1.134623 2012-02-29 -1.561819 2012-03-31 -0.260838 2012-04-30 0.281957 2012-05-31 1.523962 Freq: M, dtype: float64 # to_period()默認頻率為M,to_period和to_timestamp可互相轉換 In [120]: ps = ts.to_period() In [121]: ps Out[121]: 2012-01 -1.134623 2012-02 -1.561819 2012-03 -0.260838 2012-04 0.281957 2012-05 1.523962 Freq: M, dtype: float64 In [122]: ps.to_timestamp() Out[122]: 2012-01-01 -1.134623 2012-02-01 -1.561819 2012-03-01 -0.260838 2012-04-01 0.281957 2012-05-01 1.523962 Freq: MS, dtype: float64 ``` - 在期間和時間戳之間進行轉換可以使用一些方便的算術函數。以下範例為,將季度頻率轉換為個季最後一個月的上午9點: ```python In [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV') In [124]: ts = pd.Series(np.random.randn(len(prng)), prng) In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9 In [126]: ts.head() Out[126]: 1990-03-01 09:00 -0.902937 1990-06-01 09:00 0.068159 1990-09-01 09:00 -0.057873 1990-12-01 09:00 -0.368204 1991-03-01 09:00 -1.144073 Freq: H, dtype: float64 ``` ## Categoricals 分類 - 現在pandas可以在DataFrame中包含分類數據,詳情參閱[categorical introduction](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#categorical) 及[API documentation](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#api-arrays-categorical). ```python In [127]: df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']}) ``` - 將原始成績轉換為分類數據 ```python In [128]: df["grade"] = df["raw_grade"].astype("category") In [129]: df["grade"] Out[129]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, dtype: category Categories (3, object): [a, b, e] ``` - 重命名分類使其更有意義(使用` Series.cat.categories`轉換). ```python In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"] ``` - 重新整理類別,並添加缺少的類別(預設為回傳新 Series). ```python In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) In [132]: df["grade"] Out[132]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good] ``` - 按整理後的類別排序`.sort_values()` ```python In [133]: df.sort_values(by="grade") Out[133]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good ``` - 按類別分類也會包含具空值的類別 ```python In [134]: df.groupby("grade").size() Out[134]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64 ``` ## Plotting 繪圖 請參閱[Plotting](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization)文檔。 ```python In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) In [136]: ts = ts.cumsum() In [137]: ts.plot() Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x7f24a8b314d0> ``` ![](https://i.imgur.com/N5k8wXV.png) - 在DataFrame上,該`plot()`方法可以方便地使用標籤繪製所有列: ```python In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D']) In [139]: df = df.cumsum() In [140]: plt.figure() Out[140]: <Figure size 640x480 with 0 Axes> In [141]: df.plot() Out[141]: <matplotlib.axes._subplots.AxesSubplot at 0x7f24a8b13750> In [142]: plt.legend(loc='best') Out[142]: <matplotlib.legend.Legend at 0x7f24a88250d0> ``` ![](https://i.imgur.com/J2YfhDD.png) ## Getting data in/out 資料讀取、輸出 ### CSV - [Writing to a csv file](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-store-in-csv) ```python df.to_csv('foo.csv') ``` - [Reading from a csv file](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-read-csv-table) ```python In [144]: pd.read_csv('foo.csv') Out[144]: Unnamed: 0 A B C D 0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 4 2000-01-05 0.578117 0.511371 0.103552 -2.428202 .. ... ... ... ... ... 995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 5 columns] ``` ### HDF5 讀寫[HDFStores](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-hdf5) - 寫入HDF5儲存資料 ```python df.to_hdf('foo.h5', 'df') ``` - 讀取HDF5格式資料 ```python In [146]: pd.read_hdf('foo.h5', 'df') Out[146]: A B C D 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 2000-01-05 0.578117 0.511371 0.103552 -2.428202 ... ... ... ... ... 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 4 columns] ``` ### Excel 讀寫[MS Excel](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-excel) - 寫入excel檔案 ```python In [147]: df.to_excel('foo.xlsx', sheet_name='Sheet1') ``` - 讀取excel檔案 ```python In [148]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) Out[148]: Unnamed: 0 A B C D 0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 4 2000-01-05 0.578117 0.511371 0.103552 -2.428202 .. ... ... ... ... ... 995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 5 columns] ``` ## Gotchas 小陷阱 - 如果操作時遇到異常,如: ```python >>> if pd.Series([False, True, False]): ... print("I was true") Traceback ... ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all(). ``` - 請查看[Comparisons](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-compare)來處理異常,或查看[Gotchas](https://pandas.pydata.org/pandas-docs/stable/user_guide/gotchas.html#gotchas)也可以.