dic = {'one':[1,3,2,5,4],'two':[2,4,3,6,5],'three':[3,7,5,6,4]}
df = pd.DataFrame(dic,index=list('abcde'))
print(df)
print(df.quantile(0.1))
print(df.quantile([0.5,0.7]))
#    one  two  three
# a    1    2      3
# b    3    4      7
# c    2    3      5
# d    5    6      6
# e    4    5      4
# one      1.4
# two      2.4
# three    3.4
# Name: 0.1, dtype: float64
#      one  two  three
# 0.5  3.0  4.0    5.0
# 0.7  3.8  4.8    5.8

dic = {'one':[1,3,2,5,4],'two':[2,4,3,6,5]}
df = pd.DataFrame(dic,index=list('abcde'))
df['one_cumsum'] = df['one'].cumsum()  #相当于增加一列
df['one_cumprod'] = df['one'].cumprod()
df['two_cummax'] = df['two'].cummax()
df['two_cummin'] = df['two'].cummin()
print(df)
#    one  two  one_cumsum  one_cumprod  two_cummax  two_cummin
# a    1    2           1            1           2           2
# b    3    4           4            3           4           2
# c    2    3           6            6           4           2
# d    5    6          11           30           6           2
# e    4    5          15          120           6           2

s = pd.Series(list('abaefb'))
print(s)
sq = s.unique()
print(sq,type(sq))
sq_s = pd.Series(sq)
# 0    a
# 1    b
# 2    a
# 3    e
# 4    f
# 5    b
# dtype: object
# ['a' 'b' 'e' 'f'] <class 'numpy.ndarray'>

s = pd.Series(list('abaefb'))
s_count = s.value_counts()
print(s)
print(s_count,type(s_count))
# 0    a
# 1    b
# 2    a
# 3    e
# 4    f
# 5    b
# dtype: object
# a    2
# b    2
# f    1
# e    1
# dtype: int64 <class 'pandas.core.series.Series'>

# s = pd.Series(list('abced'))
# df = pd.DataFrame(np.arange(6).reshape(2,3),columns=['a','b','c'])
# print(s.isin(['a','b']))
# print(df.isin([1,2]))
# 0     True
# 1     True
# 2    False
# 3    False
# 4    False
# dtype: bool
#        a      b      c
# 0  False   True   True
# 1  False  False  False