author |B. Chen compile |VK source |Towards Data Science
Pandas DataFrame There's a built-in method sort_values(), You can sort values according to a given variable . The method itself is quite simple to use , But it doesn't work with custom sort , for example ,
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t T-shirt size ：XS、S、M、L and XL
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month ： January 、 February 、 March 、 April, etc
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What day ： Monday 、 Tuesday 、 Wednesday 、 Thursday 、 Friday 、 Saturday and Sunday .
In this paper , We will learn how to deal with Pandas DataFrame Custom sort .
Please check my Github repo To get the source code :https://github.com/BindiChen/machine-learning/blob/master/data-analysis/017-pandas-custom-sort/pandas-custom-sort.ipynb
problem
Suppose we have a data set about clothing stores ：
df = pd.DataFrame({
'cloth_id': [1001, 1002, 1003, 1004, 1005, 1006],
'size': ['S', 'XL', 'M', 'XS', 'L', 'S'],
})
We can see , Each piece of cloth has a size value , The data should be sorted in the following order ：
-
XS For extra large
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S For Trumpet
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M For medium
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L For big
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XL For extra large
however , When calling sort_values('size') when , You will get the following output .
The output is not what we want , But it's technically correct . actually ,sort_values() It is to sort numerical data in numerical order , Sort the object data in alphabetical order .
Here are two common solutions ：
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Create a new column for a custom sort
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Use CategoricalDtype Cast data to an ordered category type
Create a new column for a custom sort
In this solution , A mapping data frame is needed to represent a custom sort , Then create a new column from the map , Finally, we can sort the data by new columns . Let's take an example to see how this works .
First , Let's create a mapping data frame to represent a custom sort .
df_mapping = pd.DataFrame({
'size': ['XS', 'S', 'M', 'L', 'XL'],
})
sort_mapping = df_mapping.reset_index().set_index('size')
after , Use sort_mapping Create a new column with the mapping values in size_num.
df['size_num'] = df['size'].map(sort_mapping['index'])
Last , Sort values by new column size .
df.sort_values('size_num')
This, of course, is our job . But it creates an alternate column , Efficiency may be reduced when dealing with large data sets .
We can use CategoricalDtype To solve this problem more effectively .
Use CategoricalDtype Cast data to an ordered category type
CategoricalDtype Is a type of categorical data with a category and order [1]. It's very useful for creating custom sorts [2]. Let's take an example to see how this works .
First , Let's import CategoricalDtype.
from pandas.api.types import CategoricalDtype
then , Create a custom category type cat_size_order
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The first parameter is set to ['XS'、'S'、'M'、'L'、'XL'] As a unique value of size .
-
The second parameter ordered=True, Think of this variable as ordered .
cat_size_order = CategoricalDtype(
['XS', 'S', 'M', 'L', 'XL'],
ordered=True
)
then , call astype(cat_size_order) Cast size data to a custom category type . By running df['size'], We can see size Column has been converted to a category type , The order is [XS<S<M<L<XL].
>>> df['size'] = df['size'].astype(cat_size_order)
>>> df['size']
0 S
1 XL
2 M
3 XS
4 L
5 S
Name: size, dtype: category
Categories (5, object): [XS < S < M < L < XL]
Last , We can call the same method to sort the values .
df.sort_values('size')
It works better . Let's see what the principle is .
Use cat Of codes Attribute access
Now? size Column has been converted to category type , We can use .cat Accessor to view the classification properties . Behind the scenes , It USES codes Property to represent the size of an ordered variable .
Let's create a new column code , So we can compare size and code values side by side .
df['codes'] = df['size'].cat.codes
df
We can see XS、S、M、L and XL The codes for are 0、1、2、3、4 and 5.codes Is the actual value of the category . By running df.info(), We can see that it's actually int8.
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cloth_id 6 non-null int64
1 size 6 non-null category
2 codes 6 non-null int8
dtypes: category(1), int64(1), int8(1)
memory usage: 388.0 bytes
Sort by multiple variables
Next , Let's make things a little more complicated . here , We will sort the data frames by multiple variables .
df = pd.DataFrame({
'order_id': [1001, 1002, 1003, 1004, 1005, 1006, 1007],
'customer_id': [10, 12, 12, 12, 10, 10, 10],
'month': ['Feb', 'Jan', 'Jan', 'Feb', 'Feb', 'Jan', 'Feb'],
'day_of_week': ['Mon', 'Wed', 'Sun', 'Tue', 'Sat', 'Mon', 'Thu'],
})
Similarly , Let's create two custom category types cat_day_of_week and cat_month, And pass them on to astype().
cat_day_of_week = CategoricalDtype(
['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
ordered=True
)
cat_month = CategoricalDtype(
['Jan', 'Feb', 'Mar', 'Apr'],
ordered=True,
)
df['day_of_week'] = df['day_of_week'].astype(cat_day_of_week)
df['month'] = df['month'].astype(cat_month)
To sort by multiple variables , We just need to pass a list instead of sort_values(). for example , Press month and day_of_week Sort .
df.sort_values(['month', 'day_of_week'])
Press ustomer_id,month and day_of_week Sort .
df.sort_values(['customer_id', 'month', 'day_of_week'])
That's it , Thanks for reading .
In my, please Github Export the notebook to get the source code ：https://github.com/BindiChen/machine-learning/blob/master/data-analysis/017-pandas-custom-sort/pandas-custom-sort.ipynb
Reference
- [1] Pandas.CategoricalDtype API(https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.CategoricalDtype.html)
- [2] Pandas Categorical CategoricalDtype tutorial (https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#categorical-categoricaldtype)
Link to the original text ：https://towardsdatascience.com/how-to-do-a-custom-sort-on-pandas-dataframe-ac18e7ea5320
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