Pandas 是基于 NumPy 的一種數(shù)據(jù)處理工具，該工具為了解決數(shù)據(jù)分析任務(wù)而創(chuàng)建。Pandas 納入了大量庫和一些標(biāo)準(zhǔn)的數(shù)據(jù)模型，提供了高效地操作大型數(shù)據(jù)集所需的函數(shù)和方法。這些練習(xí)著重DataFrame和Series對象的基本操作，包括數(shù)據(jù)的索引、分組、統(tǒng)計和清洗。 友情提示：代碼雖好，自己動手才算學(xué)到。
基本操作1.導(dǎo)入 Pandas 庫并簡寫為 pd，并輸出版本號
import pandas as pdpd.__version__arr = [0, 1, 2, 3, 4]df = pd.Series(arr) # 如果不指定索引，則默認(rèn)從 0 開始dfd = {'a':1,'b':2,'c':3,'d':4,'e':5}df = pd.Series(d)dfdates = pd.date_range('today',periods=6) # 定義時間序列作為 indexnum_arr = np.random.randn(6,4) # 傳入 numpy 隨機(jī)數(shù)組columns = ['A','B','C','D'] # 將列表作為列名df1 = pd.DataFrame(num_arr, index = dates, columns = columns)df1# df = pd.read_csv('test.csv', encoding='gbk, sep=';')import numpy as npdata = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'], 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3], 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']df = pd.DataFrame(data, index=labels)dfdf.info()# 方法二# df.describe()df.iloc[:3]# 方法二#df.head(3)df.loc[:, ['animal', 'age']]# 方法二# df[['animal', 'age']]df.loc[df.index[[3, 4, 8]], ['animal', 'age']]df[df['age'] > 3]df[df['age'].isnull()]df[(df['age']>2) & (df['age']>4)]##df[df['age'].between(2, 4)]df.loc['f', 'age'] = 1.5df['visits'].sum()df.groupby('animal')['age'].mean()#插入df.loc['k'] = [5.5, 'dog', 'no', 2]# 刪除df = df.drop('k')dfdf['animal'].value_counts()df.sort_values(by=['age', 'visits'], ascending=[False, True])df['priority'] = df['priority'].map({'yes': True, 'no': False})dfdf['animal'] = df['animal'].replace('snake', 'python')df22.對每種animal的每種不同數(shù)量visits，計算平均age，即，返回一個表格，行是aniaml種類，列是visits數(shù)量，表格值是行動物種類列訪客數(shù)量的平均年齡
df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})print(df)df1 = df.loc[df['A'].shift() != df['A']]# 方法二# df1 = df.drop_duplicates(subset='A')print(df1)df = pd.DataFrame(np.random.random(size=(5, 3)))print(df)df1 = df.sub(df.mean(axis=1), axis=0)print(df1)df = pd.DataFrame(np.random.random(size=(5, 5)), columns=list('abcde'))print(df)df.sum().idxmin()df = pd.DataFrame({'A': list('aaabbcaabcccbbc'), 'B': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})print(df)df1 = df.groupby('A')['B'].nlargest(3).sum(level=0)print(df1)df = pd.DataFrame({'A': [1,2,11,11,33,34,35,40,79,99], 'B': [1,2,11,11,33,34,35,40,79,99]})print(df)df1 = df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()print(df1)df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})izero = np.r_[-1, (df['X'] == 0).to_numpy().nonzero()[0]] # 標(biāo)記0的位置idx = np.arange(len(df))df['Y'] = idx - izero[np.searchsorted(izero - 1, idx) - 1]print(df)# 方法二# x = (df['X'] != 0).cumsum()# y = x != x.shift()# df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()# 方法三# df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()#first_zero_idx = (df['X'] == 0).idxmax()# df['Y'].iloc[0:first_zero_idx] += 1df = pd.DataFrame(np.random.random(size=(5, 3)))print(df)df.unstack().sort_values()[-3:].index.tolist()df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), 'vals': [-12,345,3,1,45,14,4,-52,54,23,-235,21,57,3,87]})print(df)def replace(group): mask = group<0 group[mask] = group[~mask].mean() return groupdf['vals'] = df.groupby(['grps'])['vals'].transform(replace)print(df)df = pd.DataFrame({'group': list('aabbabbbabab'), 'value': [1, 2, 3, np.nan, 2, 3, np.nan, 1, 7, 3, np.nan, 8]})print(df)g1 = df.groupby(['group'])['value']g2 = df.fillna(0).groupby(['group'])['value'] s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()s.reset_index(level=0, drop=True).sort_index() dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B') s = pd.Series(np.random.rand(len(dti)), index=dti)s.head(10)s[s.index.weekday == 2].sum() s.resample('M').mean()s.groupby(pd.Grouper(freq='4M')).idxmax()pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', 'Budapest_PaRis', 'Brussels_londOn'], 'FlightNumber': [10045, np.nan, 10065, np.nan, 10085], 'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]], 'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', '12. Air France', ''Swiss Air'']})df37.FlightNumber列中有些值缺失了，他們本來應(yīng)該是每一行增加10，填充缺失的數(shù)值，并且令數(shù)據(jù)類型為整數(shù)
df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)dftemp = df.From_To.str.split('_', expand=True)temp.columns = ['From', 'To']df = df.join(temp)df = df.drop('From_To', axis=1)dfdf['From'] = df['From'].str.capitalize()df['To'] = df['To'].str.capitalize()df40.Airline列，有一些多余的標(biāo)點(diǎn)符號，需要提取出正確的航司名稱。舉例：'(British Airways. )' 應(yīng)該改為 'British Airways'.
df['Airline'] = df['Airline'].str.extract('([a-zA-Z\s]+)', expand=False).str.strip()df41.Airline列，數(shù)據(jù)被以列表的形式錄入，但是我們希望每個數(shù)字被錄入成單獨(dú)一列，delay_1, delay_2, …沒有的用NAN替代。
delays = df['RecentDelays'].apply(pd.Series)delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]df = df.drop('RecentDelays', axis=1).join(delays)df42.用 letters = ['A', 'B', 'C'] 和 numbers = list(range(10))的組合作為系列隨機(jī)值的層次化索引
letters = ['A', 'B', 'C']numbers = list(range(4))mi = pd.MultiIndex.from_product([letters, numbers])s = pd.Series(np.random.rand(12), index=mi)ss.index.is_lexsorted()# 方法二# s.index.lexsort_depth == s.index.nlevelss.loc[:, [1, 3]]s.loc[pd.IndexSlice[:'B', 2:]]# 方法二# s.loc[slice(None, 'B'), slice(2, None)]s.sum(level=0)#方法二#s.unstack().sum(axis=0)new_s = s.swaplevel(0, 1)print(new_s)print(new_s.index.is_lexsorted())new_s = new_s.sort_index()print(new_s)import matplotlib.pyplot as pltdf = pd.DataFrame({'xs':[1,5,2,8,1], 'ys':[4,2,1,9,6]})plt.style.use('ggplot')df.plot.scatter('xs', 'ys', color = 'black', marker = 'x')
df = pd.DataFrame({'productivity':[5,2,3,1,4,5,6,7,8,3,4,8,9], 'hours_in' :[1,9,6,5,3,9,2,9,1,7,4,2,2], 'happiness' :[2,1,3,2,3,1,2,3,1,2,2,1,3], 'caffienated' :[0,0,1,1,0,0,0,0,1,1,0,1,0]})df.plot.scatter('hours_in', 'productivity', s = df.happiness * 100, c = df.caffienated)
df = pd.DataFrame({'revenue':[57,68,63,71,72,90,80,62,59,51,47,52], 'advertising':[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9], 'month':range(12)})ax = df.plot.bar('month', 'revenue', color = 'green')df.plot.line('month', 'advertising', secondary_y = True, ax = ax)ax.set_xlim((-1,12));
原文地址：https://www.tuicool.com/articles/B77N7zz
