pandas describe 函数的参数理解及应用
2020-05-03 本文已影响0人
精灵鼠小弟_fb22
percentile：它是一个可选参数, 它是一个列表, 如数字的数据类型, 应在0到1之间。其默认值为[.25, .5, .75], 它返回第25、50和75个百分位数。
include：它也是一个可选参数, 在描述DataFrame时包括数据类型列表。其默认值为无。
exclude：它也是一个可选参数, 在描述DataFrame时不包括数据类型列表。其默认值为无。
用法：DataFrame.describe(percentiles=None, include=None, exclude=None)
info = pd.DataFrame({'categorical': pd.Categorical(['s', 't', 'u']),
'numeric': [1, 2, 3], 'object': ['p', 'q', 'r']})
print(info.describe(),'\n')
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
print(info.describe(include='all'),'\n')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top u NaN p
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
print(info.numeric.describe(),'\n')
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
print(info.describe(include=[np.number]),'\n')
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
print(info.describe(include=[np.object]),'\n')
object
count 3
unique 3
top p
freq 1
print(info.describe(include=['category']),'\n')
categorical
count 3
unique 3
top u
freq 1
print(info.describe(exclude=[np.number]),'\n')
categorical object
count 3 3
unique 3 3
top u p
freq 1 1
print(info.describe(exclude=[np.object]),'\n')
categorical numeric
count 3 3.0
unique 3 NaN
top u NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
pandas.loc函数理解及用法
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra 1 2
viper 4 5
sidewinder 7 8
Single label. Note this returns the row as a Series.
取出某列
>>> df.loc['viper']
max_speed 4
shield 5
Name: viper, dtype: int64
List of labels. Note using ``[[]]`` returns a DataFrame.
用双[[ ]]取出数据框
>>> df.loc[['viper', 'sidewinder']]
max_speed shield
viper 4 5
sidewinder 7 8
Single label for row and column
用行/列标签取某个元素
>>> df.loc['cobra', 'shield']
2
Slice with labels for row and single label for column. As mentioned
above, note that both the start and stop of the slice are included
多行标签，单列，注意是一个闭区间
>>> df.loc['cobra':'viper', 'max_speed']
cobra 1
viper 4
Name: max_speed, dtype: int64
Boolean list with the same length as the row axis
用跟行数相等长度的布尔值，来表示该行是否要取用
>>> df.loc[[False, False, True]]
max_speed shield
sidewinder 7 8
Conditional that returns a boolean Series
设定条件的返回
>>> df.loc[df['shield'] > 6]
max_speed shield
sidewinder 7 8
Conditional that returns a boolean Series with column labels specified
>>> df.loc[df['shield'] > 6, ['max_speed']]
max_speed
sidewinder 7
Callable that returns a boolean Series
用可调用的方法返回的布尔序列来取用数据
>>> df.loc[lambda df: df['shield'] == 8]
max_speed shield
sidewinder 7 8
**Setting values**
Set value for all items matching the list of labels
对能匹配标签的的项设定值
>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
>>> df
max_speed shield
cobra 1 2
viper 4 50
sidewinder 7 50
Set value for an entire row
对整行设值
>>> df.loc['cobra'] = 10
>>> df
max_speed shield
cobra 10 10
viper 4 50
sidewinder 7 50
Set value for an entire column
对全列设值，注意要在逗号后，因为逗号前表示要设定的行的范围
>>> df.loc[:, 'max_speed'] = 30
>>> df
max_speed shield
cobra 30 10
viper 30 50
sidewinder 30 50
Set value for rows matching callable condition
对满足返回值的条件的行设定值
>>> df.loc[df['shield'] > 35] = 0
>>> df
max_speed shield
cobra 30 10
viper 0 0
sidewinder 0 0
**Getting values on a DataFrame with an index that has integer labels**
Another example using integers for the index
数字索引
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=[7, 8, 9], columns=['max_speed', 'shield'])
>>> df
max_speed shield
7 1 2
8 4 5
9 7 8
Slice with integer labels for rows. As mentioned above, note that both
the start and stop of the slice are included.
>>> df.loc[7:9]
max_speed shield
7 1 2
8 4 5
9 7 8
**Getting values with a MultiIndex**
用多项索引获值
A number of examples using a DataFrame with a MultiIndex
>>> tuples = [
... ('cobra', 'mark i'), ('cobra', 'mark ii'),
... ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
... ('viper', 'mark ii'), ('viper', 'mark iii')
... ]
>>> index = pd.MultiIndex.from_tuples(tuples)
>>> values = [[12, 2], [0, 4], [10, 20],
... [1, 4], [7, 1], [16, 36]]
>>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
>>> df
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
Single label. Note this returns a DataFrame with a single index.
>>> df.loc['cobra']
max_speed shield
mark i 12 2
mark ii 0 4
Single index tuple. Note this returns a Series.
元组索引，返回序列
>>> df.loc[('cobra', 'mark ii')]
max_speed 0
shield 4
Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this
returns a Series.
单个索引，返回序列
>>> df.loc['cobra', 'mark i']
max_speed 12
shield 2
Name: (cobra, mark i), dtype: int64
Single tuple. Note using ``[[]]`` returns a DataFrame.
返回数据框
>>> df.loc[[('cobra', 'mark ii')]]
max_speed shield
cobra mark ii 0 4
Single tuple for the index with a single label for the column
一个元组索引和一个标签，返回某个元素值
>>> df.loc[('cobra', 'mark i'), 'shield']
2
Slice from index tuple to single label
索引切片，返回数据框
>>> df.loc[('cobra', 'mark i'):'viper']
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
Slice from index tuple to index tuple
元组索引：元素索引的切片，返回值同上一个
>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
数据及解析源自官方文档
