10分鐘的Pandas入門-繁中版

--- 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/user_guide/10min.html [![image](https://hackmd.io/_uploads/SJXWoCEJA.png) ](https://colab.research.google.com/github/willismax/MediaSystem-Python-Course/blob/main/01.Intro-Python/10%E5%88%86%E9%90%98Pandas.ipynb) [TOC] ## 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~~，可在官方[Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html#cookbook)看到更複雜的文件說明。 - 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 ``` ## pandas 的基本資料結構 Pandas 提供了兩種類型的類別來處理資料： 1. [`Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series "貓熊系列")：保存任何類型資料的一維數值組合。例如整數、字串、Python 物件等。 2. [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "pandas.DataFrame")：一種二維資料結構，用於保存數據，例如二維數組或具有行和列的表格。 ## Object creation 創建物件 - 參閱官方文件[Data Structure Intro section](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dsintro) - 通過傳入一個list創建[Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series)，pandas預設會產生整數的[RangeIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html#pandas.RangeIndex)。 ```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 ``` - [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "pandas.DataFrame")透過使用帶有標籤的list傳遞帶有日期時間索引的 NumPy 數組來建立[`date_range()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.date_range.html#pandas.date_range "pandas.date_range") ： ```python # In[6]: 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 # In[7] df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) df # Out[7]: 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 ``` > `np.random.randn(6, 4)` 是一個 NumPy 函數，用於生成一個形狀為 (6, 4) 的數組，其中的元素來自於標準常態分佈（均值為 0，標準差為 1）。這個函數是為了方便從 Matlab 移植代碼而設計的，並且將標準常態分佈的生成封裝在了 `standard_normal` 函數中。 - [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "pandas.DataFrame")以字典`dict`:`{Key:Value}`創建`DataFrame`，其中Key是列標籤、Value是列之值。 ```python # In[8]: 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[8]: 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. ``` [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "pandas.DataFrame")欄位可以有不同的資料結構 [dtypes](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html#basics-dtypes)： ```python # In [09]: df2.dtypes # Out[09]: 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.columns df2.align df2.copy df2.all df2.count df2.any df2.combine df2.append df2.D df2.apply df2.describe df2.applymap df2.diff df2.B df2.duplicated ... ``` ## Viewing data 檢視資料 - 參閱[Basics section](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html#basics) - 以[`DataFrame.head()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head "pandas.DataFrame.head")查看DataFrame的前n筆資料，[`DataFrame.tail()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html#pandas.DataFrame.tail "pandas.DataFrame.tail")查看最後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[`DataFrame.index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.index.html#pandas.DataFrame.index "pandas.DataFrame.index")、[`DataFrame.columns`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.columns.html#pandas.DataFrame.columns "pandas.DataFrame.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 ``` > `pd.series.std(ddof=1)`預設為樣本的標準差，如果要像`numpy.std`以母體為標準差，應改為`pd.series.std(ddof=0)` - 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 ``` - [`DataFrame.sort_values()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values "pandas.DataFrame.sort_values")按值排序： ```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[`DataFrame.at()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at "pandas.DataFrame.at")、[`DataFrame.iat()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iat.html#pandas.DataFrame.iat "pandas.DataFrame.iat")和。[`DataFrame.loc()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html#pandas.DataFrame.loc "pandas.DataFrame.loc") [`DataFrame.iloc()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html#pandas.DataFrame.iloc "pandas.DataFrame.iloc") 。參閱文件[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 - [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame "pandas.DataFrame") 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 ``` ### 以標籤進行選擇請參閱[按標籤選擇](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-label)了解更多內容。[`DataFrame.loc()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html#pandas.DataFrame.loc "pandas.DataFrame.loc") [`DataFrame.at()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at "pandas.DataFrame.at") - 選擇與標籤相符的行： ```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 ``` - 快速存取標量（相當於先前的方法）： ```python In [31]: df.at[dates[0], 'A'] Out[31]: 0.4691122999071863 ``` ### Selection by position 以位置選擇 > `loc`以標籤取得Rows數據，`iloc`以行號取得數據。 - 在[Selection by Position](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-integer)查看更多內容。[`DataFrame.iloc()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html#pandas.DataFrame.iloc "pandas.DataFrame.iloc") [`DataFrame.iat()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iat.html#pandas.DataFrame.iat "pandas.DataFrame.iat") - 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 ``` - 快速取得特定值（相當於先前的方法）: ```python 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 ``` - 顯示DataFrame滿足布林條件的情形 ```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 ``` > `df.copy()`預設為`df.copy(deep=True)`，意思是預設執行深Copy > 深複製：創建一個新的物件，並且徹底複製原始物件中的所有數據。深複製後，原始數據和新複製的數據互不影響，它們在記憶體中是完全獨立的。 > 淺複製：創建一個新的物件，但是不會徹底複製數據，只是複製數據的引用。淺複製後，原始數據和新複製的數據會相互影響，因為它們共享同一塊記憶體中的數據。 > `df2 = df` 會是淺複製，`df2` 的任何改動也會變更 `df` ，因為是同一筆數據集。 ### Setting 設置 - 設定新列會自動按索引對齊資料： ```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 ``` - Series或DataFrame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)，並且會用`np.nan`填滿未對齊的標籤。 ```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 ``` ### 使用者定義的函數 - 應用`DataFrame.agg()`、`DataFrame.transform()`使用者定義的函數來分別減少或廣播其結果。 ```python In [66]: df.agg(lambda x: np.mean(x) * 5.6) Out[66]: A -0.025054 B -2.150294 C -3.851445 D 28.000000 F 16.800000 dtype: float64 In [67]: df.transform(lambda x: x * 101.2) Out[67]: A B C D F 2013-01-01 0.000000 0.000000 -152.716721 506.0 NaN 2013-01-02 122.665737 -17.529322 12.063922 506.0 101.2 2013-01-03 -87.219115 -212.982405 -50.086843 506.0 202.4 2013-01-04 73.021382 -71.525239 -105.204988 506.0 303.6 2013-01-05 -43.007200 57.382459 27.954680 506.0 404.8 2013-01-06 -68.177398 11.501219 -149.616767 506.0 506.0 ``` ### Value 很重要 - 更多資訊請參見[直方圖和離散化](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html#basics-discretization)。 ```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 2 2 6 2 1 1 Name: count, dtype: int64 ``` ### 字串方法 - [Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series)具有字串`str`的處理方法，可以方便地對數組的每個元素進行操作，如下面的程式碼片段所示。請參閱[向量化字串方法](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#text-string-methods)以了解更多資訊。 - 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） ```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 ``` ### 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 ``` ## 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 ``` > 向[DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)新增"Column"相對較快。但是，添加"Row"需要copy副本，並且可能很昂貴。我們建議將預先建立的記錄列表傳遞給DataFrame建構函數，而不是DataFrame透過迭代地向其追加記錄來建構。 ### Join - [merge()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html#pandas.merge) 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 ``` - [merge()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html#pandas.merge)唯一值: ```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 ``` ## 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 ``` - 按列標籤分組，選擇列標籤，然後將 [DataFrameGroupBy.sum()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.sum.html#pandas.core.groupby.DataFrameGroupBy.sum)函數套用至結果組： ```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 - 分層資料結構的形式 ```python In [91]: arrays = [ ....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ....: ["one", "two", "one", "two", "one", "two", "one", "two"], ....: ] ....: In [92]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"]) In [93]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"]) In [94]: df2 = df[:4] In [95]: df2 Out[95]: A B first second bar one -0.727965 -0.589346 two 0.339969 -0.693205 baz one -0.339355 0.593616 two 0.884345 1.591431 ``` - 使用[stack()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html#pandas.DataFrame.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[MultiIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html#pandas.MultiIndex)），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 ``` - [Series.tz_convert()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.tz_convert.html#pandas.Series.tz_convert)將轉換為另一個時區：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 ``` - [BusinessDay](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.tseries.offsets.BusinessDay.html#pandas.tseries.offsets.BusinessDay)在不同時間跨度表示間轉換： ```python In [113]: rng Out[113]: DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09', '2012-03-10'], dtype='datetime64[ns]', freq='D') In [114]: rng + pd.offsets.BusinessDay(5) Out[114]: DatetimeIndex(['2012-03-13', '2012-03-14', '2012-03-15', '2012-03-16', '2012-03-16'], dtype='datetime64[ns]', freq=None) ``` ## 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 [124]: import matplotlib.pyplot as plt In [125]: plt.close("all") ``` - 此plt.close方法用於關閉圖形視窗： ```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) > 使用 Jupyter 時，繪圖將使用出現plot()。否則使用 matplotlib.pyplot.show顯示它或 matplotlib.pyplot.savefig將其寫入檔案。 - 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 = pd.DataFrame(np.random.randint(0, 5, (10, 5))) 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 [136]: pd.read_csv("foo.csv") Out[136]: Unnamed: 0 0 1 2 3 4 0 0 4 3 1 1 2 1 1 1 0 2 3 2 2 2 1 4 2 1 2 3 3 0 4 0 2 2 4 4 4 2 2 3 4 5 5 4 0 4 3 1 6 6 2 1 2 0 3 7 7 4 0 4 4 4 8 8 4 4 1 0 1 9 9 0 4 3 0 3 ``` ### Parquet ```python In [137]: df.to_parquet("foo.parquet") ``` ```python In [138]: pd.read_parquet("foo.parquet") Out[138]: 0 1 2 3 4 0 4 3 1 1 2 1 1 0 2 3 2 2 1 4 2 1 2 3 0 4 0 2 2 4 4 2 2 3 4 5 4 0 4 3 1 6 2 1 2 0 3 7 4 0 4 4 4 8 4 4 1 0 1 9 0 4 3 0 3 ``` ### Excel 讀寫[MS Excel](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-excel) - 寫入excel檔案[DataFrame.to_excel()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html#pandas.DataFrame.to_excel) ```python In [147]: df.to_excel('foo.xlsx', sheet_name='Sheet1') ``` - 讀取excel檔案[read_excel()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html#pandas.read_excel) ```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.

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