8.2.6 データの集計

describeメソッドは、各列のデータの中心傾向、分散および形状を要約する記述統計を計算します。

また、結果に含める列または結果から除外する列の型を指定することもできます。

sumおよびcumsumメソッドを使用すると、oml.DataFrameの各Float列またはBoolean列の合計および累積合計を計算できます。

describeメソッドは、次の統計の検出をサポートしています。

• 平均、最小、最大、中央値、上位文字、標準偏差

• Null以外の値、一意の値、上位文字の数

• 0から1の間のパーセンタイル

例8-12 記述統計の計算

次の例にこれらの操作を示します。

import pandas as pd
import oml

df = pd.DataFrame({'numeric': [1, 1.4, -4, 3.145, 5, None],
                   'string' : [None, None, 'a', 'a', 'a', 'b'],
                   'bytes' : [b'a', b'b', b'c', b'c', b'd', b'e']})

oml_df = oml.push(df, dbtypes = {'numeric': 'BINARY_DOUBLE',
                                 'string':'CHAR(1)', 
                                 'bytes':'RAW(1)'})

# Combine a Boolean column with oml_df.
oml_bool = oml_df['numeric'] > 3
oml_df = oml_df.concat(oml_bool)
oml_df.rename({'COL4':'boolean'})

# Describe all of the columns.
oml_df.describe(include='all')

# Exclude Float columns.
oml_df.describe(exclude=[oml.Float])

# Get the sum of values in each Float or Boolean column.
oml_df.sum()

# Find the cumulative sum of values in each Float or Boolean column
# after oml_df is sorted by the bytes column in descending order.
oml_df.cumsum(by = 'bytes', ascending = False)

# Compute the skewness of values in the Float columns.
oml_df.skew()

# Find the median value of Float columns.
oml_df.median()

# Calculate the kurtosis of Float columns.
oml_df.kurtosis()

この例のリスト

>>> import pandas as pd
>>> import oml
>>>
>>> df = pd.DataFrame({'numeric': [1, 1.4, -4, 3.145, 5, None],
...                    'string' : [None, None, 'a', 'a', 'a', 'b'],
...                    'bytes' : [b'a', b'b', b'c', b'c', b'd', b'e']})
>>> 
>>> oml_df = oml.push(df, dbtypes = {'numeric': 'BINARY_DOUBLE',
...                                  'string':'CHAR(1)', 
...                                  'bytes':'RAW(1)'})
>>> 
>>> # Combine a Boolean column with oml_df.
... oml_bool = oml_df['numeric'] > 3
>>> oml_df = oml_df.concat(oml_bool)
>>> oml_df.rename({'COL4':'boolean'})
  bytes  numeric string  boolean
0  b'a'    1.000   None    False
1  b'b'    1.400   None    False
2  b'c'   -4.000      a    False
3  b'c'    3.145      a     True
4  b'd'    5.000      a     True
5  b'e'      NaN      b     True
>>>
>>> # Describe all of the columns.
... oml_df.describe(include='all')
       bytes   numeric string boolean
count      6  5.000000      4       6
unique     5       NaN      2       2
top     b'c'       NaN      a   False
freq       2       NaN      3       3
mean     NaN  1.309000    NaN     NaN
std      NaN  3.364655    NaN     NaN
min      NaN -4.000000    NaN     NaN
25%      NaN  1.000000    NaN     NaN
50%      NaN  1.400000    NaN     NaN
75%      NaN  3.145000    NaN     NaN
max      NaN  5.000000    NaN     NaN
>>>
>>> # Exclude Float columns.
... oml_df.describe(exclude=[oml.Float])
       bytes string boolean
count      6      4       6
unique     5      2       2
top     b'c'      a   False
freq       2      3       3
>>> 
>>> # Get the sum of values in each Float or Boolean column.
... oml_df.sum()
numeric    6.545
boolean    3.000
dtype: float64
>>>
>>> # Find the cumulative sum of values in each Float or Boolean column
... # after oml_df is sorted by the bytes column in descending order.
... oml_df.cumsum(by = 'bytes', ascending = False)
   numeric  boolean
0      NaN        1
1    5.000        2
2    1.000        2
3    4.145        3
4    5.545        3
5    6.545        3
>>>
>>> # Compute the skewness of values in the Float columns.
... oml_df.skew()
numeric   -0.683838
dtype: float64
>>>
>>> # Find the median value of Float columns.
... oml_df.median()
numeric    1.4
dtype: float64
>>>
>>> # Calculate the kurtosis of Float columns.
... oml_df.kurtosis()
numeric   -0.582684
dtype: float64