pandas-python入门基操

import numpy as npimport pandas as pd# ---------------------------------------------------------------#   目录#       生成数据#       查看数据#       选择#       缺失值#       运算-apply#       合并#       分组#       重塑-reshape#       数据透视表#       时间序列#       类别-Category#       csv数据输入/输出# --------------------------------------------------------------

s = pd.Series([1,3,5,np.nan,6,8])

# ---------------------------------------------------------------#   生成数据#   https://www.pypandas.cn/docs/getting_started/dsintro.html#series# ---------------------------------------------------------------dates = pd.date_range(‘20130101‘,periods=6)df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list(‘ABCD‘))df2 = pd.DataFrame({‘A‘:1.0,                    ‘B‘:pd.Timestamp(‘20190102‘),                    ‘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‘})

# ---------------------------------------------------------------#   查看数据# ---------------------------------------------------------------df2.to_numpy()df2.describe()df2.T  # 转置df2.sort_index(axis=1,ascending=False)  # axis = 1>按照列排序,ascending> 升序df2.sort_values(by=‘B‘,ascending=False)

# ---------------------------------------------------------------#   选择数据-筛选#   索引与选择数据：https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing#   多层索引与高级索引：https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced# ---------------------------------------------------------------    # 按标签选择df.A   #  等价 df[‘A‘]df2[0:3]  #第0行到第3行df.loc[dates[0]]df.loc[:,[‘A‘,‘B‘]]  # df.loc[‘20130101‘:‘20130103‘,[‘A‘,‘B‘]]df.loc[‘20130101‘,[‘A‘,‘B‘]]    # 按位置选择df.iloc[0:3,1:2]  # 0-3行，1-2列df.iloc[[1,2,4],[0,2]]    # 布尔索引df[df.A>0]   # 按行筛选df[df>0]    # isin 查找df[‘E‘] = [‘one‘, ‘one‘, ‘two‘, ‘three‘, ‘four‘, ‘three‘]df[df[‘E‘].isin([‘one‘,‘two‘])]

# ---------------------------------------------------------------#   赋值# ---------------------------------------------------------------    # 用索引自动对齐新增列的数据s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range(‘20190102‘,periods=6))df[‘F‘] = s1 # 长度和列一样    # 按照标签赋值df.at[dates[0],‘A‘] = 0 # 锁定一行数据    # 用where条件赋值df3 = df.copy()# df[df>0] = -df3

# ---------------------------------------------------------------#   缺失值# ---------------------------------------------------------------df.dropna(how=‘any‘)  # 删除有空行的行df.fillna(value=5)pd.isna(df)

# ---------------------------------------------------------------#   运算#   字符串：https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#text-string-methods#   二进制操作： https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-binop# ---------------------------------------------------------------df.mean()   # 平均值，按照列df.mean(1)  #平均值，按照行去组织

s1 = pd.Series([1,3,5,np.nan,6,8],index = dates).shift(2)  # shift按照纵轴方向移动df.drop([‘E‘,‘F‘],axis=1,inplace=True)  # 删除两列df.sub(s1, axis= ‘index‘)

df.apply(np.cumsum)# df.apply(lambda x : x.max()-x.min,axis=1)

# Series 可以调用str方法中的lower转换为小写办法  s1.str.lower()

# ---------------------------------------------------------------#   合并#   https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging# ---------------------------------------------------------------    # Concatcn1 = pd.DataFrame(np.random.randn(10,4))pieces = [cn1[:3],cn1[3:7],cn1[7:]]pd.concat(pieces)

    # Joinleft = pd.DataFrame({‘key‘:[‘foo‘,‘foo‘],‘lval‘:[1,2]})right = pd.DataFrame({‘key‘:[‘foo‘,‘foo‘],‘rval‘:[4,6]})pd.merge(left,right,on=‘key‘)

    # 追加append = pd.DataFrame(np.random.randn(8,4),columns=[‘A‘,‘B‘,‘C‘,‘D‘])append1 = append.iloc[3]append.append(append1,ignore_index=True)

# ---------------------------------------------------------------#   分组  group by ,有三个步骤-分割、应用、组合#   https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#groupby#   分割：按条件把数据分割成多组#   应用：为魅族单独应用函数#   组合：将处理结果组合成一个数据结构# ---------------------------------------------------------------group = pd.DataFrame({    ‘A‘:[‘foo‘,‘bar‘,‘foo‘,‘bar‘,‘foo‘,‘bar‘,‘foo‘,‘foot‘],    ‘B‘:[‘one‘,‘one‘,‘two‘,‘three‘,‘two‘,‘two‘,‘one‘,‘three‘],    ‘C‘:np.random.randn(8),    ‘D‘:np.random.randn(8)})group_result = group.groupby(by=[‘A‘,‘B‘]).sum()

# ---------------------------------------------------------------#   重塑# ---------------------------------------------------------------    # 堆叠    # 可以看成是解压和压缩的区别，zip相当与压缩  zip（*）相当于解压。，生成元组对stack_tuples = list(zip(*[[‘bar‘,‘bar‘,‘baz‘,‘baz‘,‘foo‘,‘foo‘,‘qux‘,‘qux‘],                          [‘one‘,‘two‘,‘one‘,‘two‘,‘one‘,‘two‘,‘one‘,‘two‘]]))index = pd.MultiIndex.from_tuples(tuples=stack_tuples,names=[‘first‘,‘second‘])df_stack = pd.DataFrame(np.random.randn(8,2),index = index,columns=[‘A‘,‘B‘])df_stack = df_stack[:4]# 压缩后的 DataFrame 或 Series 具有多层索引， stack() 的逆操作是 unstack()，默认为拆叠最后一层stacked = df_stack.stack()  # 将数据展示到一列上  unstack()是stack()的逆操作stacked.unstack(1)  # 1是指的第几层索引

# ---------------------------------------------------------------#   数据透视表  pivot_table#   https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html#reshaping-pivot# ---------------------------------------------------------------pivot_table_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)})pivot_table_df.pivot_table(index=[‘A‘,‘B‘],columns=‘C‘)

# ---------------------------------------------------------------#   时间序列  pivot_table#   https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries# ---------------------------------------------------------------    # freq = ‘S‘ 时间格式：2019-01-01 00:00:04    # freq = ‘D‘ 时间格式：2019-01-01    # freq参数： https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesrng = pd.date_range(‘1/1/2019‘,periods=100,freq=‘S‘)ts = pd.Series(np.random.randint(0,500,len(rng)),index= rng)    # 转换成其他时区tz_rng = pd.date_range(‘1/1/2019‘,periods=5,freq=‘M‘)ts_tz_rng = pd.Series(np.random.randn(len(tz_rng)),index = tz_rng)    # ts_tz_rng.to_period()  将时间转换为 yyyy-mm格式prng = pd.period_range(‘1991Q1‘,‘2000Q4‘,freq=‘Q-NOV‘)ts_prng = pd.Series(np.random.randn(len(prng)),prng)    # 频率转换  https://blog.csdn.net/bqw18744018044/article/details/80947243ts_prng.index = (prng.asfreq(‘M‘,‘e‘)+1).asfreq(‘H‘,‘s‘)+9  # 切换1991Q1 -> 1991-03-01 09:00

# ---------------------------------------------------------------#   类型Categories#   https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#categorical#   https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#api-arrays-categorical  -- api# ---------------------------------------------------------------cate_df = pd.DataFrame({    ‘id‘:[1,2,3,4,5,6],    ‘raw_grade‘:[‘a‘,‘b‘,‘b‘,‘a‘,‘a‘,‘e‘]})cate_df[‘grade‘] = cate_df[‘raw_grade‘].astype(‘category‘)    # 重命名不同类型cate_df[‘grade‘].cat.categories = [‘very good‘,‘good‘,‘very bad‘]

# ---------------------------------------------------------------#   可视化文档#   https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization# ---------------------------------------------------------------ts_plot = pd.Series(np.random.randn(1000),index=pd.date_range(‘1/1/2000‘,periods=1000))ts_plot = ts_plot.cumsum()ts_plot.plot()

# ---------------------------------------------------------------#   CSV处理#   https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-store-in-csv# ---------------------------------------------------------------#df2.to_csv(‘d:\\foo.csv‘)  # 存储到csv中df2.to_excel(‘d:\\foo.xlsx‘,‘sheet1‘,index_col= None,na_values=[‘NA‘])

# 错误 https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-compare

原文地址：https://www.cnblogs.com/wind-man/p/12178633.html