Pandas high-order uses the -transform method

The transform() method is used in pandas

The official explanation

DataFrame.transform(self, func, axis=0, * * kwargs) - > * args'DataFrame'[source]
Call func on self producing a DataFrame with transformed values.

Produced DataFrame will have same axis length as self.
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• funcfunction, str, list or dict Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

- function
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• string function name

• list of functions and/or function names, e.g. [np.exp. 'sqrt']

• dict of axis labels -> functions, function names or list of such.

• axis

{0 or ‘index’, 1 or ‘columns’}, default 0 If 0 or’ index ‘: apply function to each column. If 1 or’ columns’ : apply function to each row.

• *args

Positional arguments to pass to func.

• **kwargs

Keyword arguments to pass to func.

• Returns: DataFrame

A DataFrame that must have the same length as self.

• Raises: ValueError

If the returned DataFrame has a different length than self.

import numpy as np 
import pandas as pd
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The transform method

The characteristics of

The transform method is usually used in conjunction with the groupby method

• Generates a scalar value and broadcasts it into the size data of each grouping
• Transform produces an object of the same size as the input
• Transform cannot change its input

df = pd.DataFrame({
    "key": ["a"."b"."c"] * 4.    "values":np.arange(12.0)
})
df
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grouping

g = df.groupby("key").values  # Group and average
g.mean()
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Key a 4.5B 5.5C 6.5 Name: values, dType: FLOAT64Copy the code

The transform using

Each position is replaced by the mean

Passing anonymous functions

g.transform(lambda x:x.mean())
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0     4.5
1     5.5
2     6.5
3     4.5
4     5.5
5     6.5
6     4.5
7     5.5
8     6.5
9     4.5
10    5.5
11    6.5
Name: values, dtype: float64
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Pass the function string alias in the AGG method

The built-in aggregate function passes the alias name directly, Max \min\sum\mean

g.transform("mean")
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0     4.5
1     5.5
2     6.5
3     4.5
4     5.5
5     6.5
6     4.5
7     5.5
8     6.5
9     4.5
10    5.5
11    6.5
Name: values, dtype: float64
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Tranform is used with functions that return S

g.transform(lambda x:x * 2)
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0 0.0 12.0 2 4.0 3 6.0 4 8.0 5 10.0 6 12.0 7 14.0 8 16.0 9 18.0 10 20.0 11 22.0 Name: VALUES, DTYPE: FLOAT64Copy the code

Descending rank

g.transform(lambda x:x.rank(ascending=False))
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0     4.0
1     4.0
2     4.0
3     3.0
4     3.0
5     3.0
6     2.0
7     2.0
8     2.0
9     1.0
10    1.0
11    1.0
Name: values, dtype: float64
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Similar to Apply

Pass functions directly into TranForm

def normalize(x):
    return (x - x.mean()) / x.std()

g.transform(normalize)   
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0-1.161895 1-1.161895 2-1.161895 3-0.387298 4-0.387298 5-0.387298 6 0.387298 7 0.387298 8 0.387298 9 1.161895 10 1.161895 11 1.161895 Name: values, dType: FLOAT64Copy the code

g.apply(normalize)  # result same as above
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0-1.161895 1-1.161895 2-1.161895 3-0.387298 4-0.387298 5-0.387298 6 0.387298 7 0.387298 8 0.387298 9 1.161895 10 1.161895 11 1.161895 Name: values, dType: FLOAT64Copy the code

Normalized instance

normalized = (df["values"] - g.transform("mean")) / g.transform("std") # The built-in aggregate function is passed directly
normalized
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0-1.161895 1-1.161895 2-1.161895 3-0.387298 4-0.387298 5-0.387298 6 0.387298 7 0.387298 8 0.387298 9 1.161895 10 1.161895 11 1.161895 Name: values, dType: FLOAT64Copy the code

The official instance

df = pd.DataFrame({'A': range(3), 'B': range(1.4)})
df
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df.transform(lambda x:x+1)  +1 for each element
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s = pd.Series(range(3))
s
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0    0
1    1
2    2
dtype: int64
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s.transform([np.sqrt, np.exp])  Pass in the function
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Understanding the Transform Function in Pandas

There is a complete example on this site that explains the use of the Transform method

The original data

To solve the problem

You can see in the data that the file contains 3 different orders (10001, 10005 and 10006) and that each order consists has multiple products (aka skus).

The question we would like to answer is: “What percentage of The order total does each sku represent?”

For example, if we look at order 10001 with a total of $576.12, the break down would be:

• B1-20000 = $235.83 or 40.9%
• S1-27722 = $232.32 or 40.3%
• B1-86481 = $107.97 or 18.7%

Figure out the percentage of each item’s price in the order

The traditional method

Calculate the proportion of a column and merge it with the original data

import pandas as pd
df = pd.read_excel("sales_transactions.xlsx")

df.groupby('order') ["ext price"].sum()

order 10001 576.12 10005 8185.49 10006 3724.49 Name: ext price, dtype: float64 Copy the code

order_total = df.groupby('order') ["ext price"].sum().rename("Order_Total").reset_index()  # Add the value of the Order_Total column attribute
df_1 = df.merge(order_total)    # Merge raw df and ORDER_total data
df_1["Percent_of_Order"] = df_1["ext price"] / df_1["Order_Total"]  # add Percent_of_Order
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The use of the transform

Transform + groupby: Group and then sum

Graphic transform