Pandas 是基于 NumPy 的一种数据处理工具,该工具为了解决数据分析任务而创建。Pandas 纳入了大量库和一些标准的数据模型,提供了高效地操作大型数据集所需的函数和方法。这些练习着重DataFrame和Series对象的基本操作,包括数据的索引、分组、统计和清洗。 友情提示:代码虽好,自己动手才算学到。
基本操作1.导入 Pandas 库并简写为 pd,并输出版本号import pandas as pdpd.__version__
arr = [0, 1, 2, 3, 4]df = pd.Series(arr) # 如果不指定索引,则默认从 0 开始df
3.从字典创建 Seriesd = {'a':1,'b':2,'c':3,'d':4,'e':5}df = pd.Series(d)df
dates = pd.date_range('today',periods=6) # 定义时间序列作为 indexnum_arr = np.random.randn(6,4) # 传入 numpy 随机数组columns = ['A','B','C','D'] # 将列表作为列名df1 = pd.DataFrame(num_arr, index = dates, columns = columns)df1
5.从CSV中创建 DataFrame,分隔符为;,编码格式为gbk# 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)df
7.显示DataFrame的基础信息,包括行的数量;列名;每一列值的数量、类型df.info()# 方法二# df.describe()
df.iloc[:3]# 方法二#df.head(3)
9.取出df的animal和age列df.loc[:, ['animal', 'age']]# 方法二# df[['animal', 'age']]
df.loc[df.index[[3, 4, 8]], ['animal', 'age']]
11.取出age值大于3的行df[df['age'] > 3]
df[df['age'].isnull()]
13.取出age在2,4间的行(不含)df[(df['age']>2) & (df['age']>4)]##df[df['age'].between(2, 4)]
df.loc['f', 'age'] = 1.5
15.计算visits的总和df['visits'].sum()
df.groupby('animal')['age'].mean()
17.在df中插入新行k,然后删除该行#插入df.loc['k'] = [5.5, 'dog', 'no', 2]# 删除df = df.drop('k')df
df['animal'].value_counts()
19.先按age降序排列,后按visits升序排列df.sort_values(by=['age', 'visits'], ascending=[False, True])
df['priority'] = df['priority'].map({'yes': True, 'no': False})df
21.将animal列中的snake替换为pythondf['animal'] = df['animal'].replace('snake', 'python')df
22.对每种animal的每种不同数量visits,计算平均age,即,返回一个表格,行是aniaml种类,列是visits数量,表格值是行动物种类列访客数量的平均年龄
df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')
进阶操作23.有一列整数列A的DatraFrame,删除数值重复的行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)
25.一个有5列的DataFrame,求哪一列的和最小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)
27.给定DataFrame,有列A, B,A的值在1-100(含),对A列每10步长,求对应的B的和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]] # 标记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] += 1
29.一个全数值的DataFrame,返回最大3值的坐标df = 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)
31.计算3位滑动窗口的平均值,忽略NANdf = 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)
33.所有礼拜三的值求和s[s.index.weekday == 2].sum()
s.resample('M').mean()
35.每连续4个月为一组,求最大值所在的日期s.groupby(pd.Grouper(freq='4M')).idxmax()
pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')
数据清洗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'']})df
37.FlightNumber列中有些值缺失了,他们本来应该是每一行增加10,填充缺失的数值,并且令数据类型为整数
df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)df
38.将From_To列从_分开,分成From, To两列,并删除原始列temp = df.From_To.str.split('_', expand=True)temp.columns = ['From', 'To']df = df.join(temp)df = df.drop('From_To', axis=1)df
df['From'] = df['From'].str.capitalize()df['To'] = df['To'].str.capitalize()df
40.Airline列,有一些多余的标点符号,需要提取出正确的航司名称。举例:'(British Airways. )' 应该改为 'British Airways'.
df['Airline'] = df['Airline'].str.extract('([a-zA-Z\s]+)', expand=False).str.strip()df
41.Airline列,数据被以列表的形式录入,但是我们希望每个数字被录入成单独一列,delay_1, delay_2, …没有的用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)df
层次化索引42.用 letters = ['A', 'B', 'C'] 和 numbers = list(range(10))的组合作为系列随机值的层次化索引
letters = ['A', 'B', 'C']numbers = list(range(4))mi = pd.MultiIndex.from_product([letters, numbers])s = pd.Series(np.random.rand(12), index=mi)s
s.index.is_lexsorted()# 方法二# s.index.lexsort_depth == s.index.nlevels
44.选择二级索引为1, 3的行s.loc[:, [1, 3]]
s.loc[pd.IndexSlice[:'B', 2:]]# 方法二# s.loc[slice(None, 'B'), slice(2, None)]
46.计算每个一级索引的和(A, B, C每一个的和)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)
可视化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
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