@@ -13,24 +13,28 @@ flexibility when built-in methods are not sufficient. These functions can be
13
13
applied at different levels: element-wise, row-wise, column-wise, or group-wise,
14
14
and change the data differently, depending on the method used.
15
15
16
- Why Use User-Defined Functions?
17
- -------------------------------
16
+ Why Not To Use User-Defined Functions
17
+ -----------------------------------------
18
18
19
- Pandas is designed for high-performance data processing, but sometimes your specific
20
- needs go beyond standard aggregation, transformation, or filtering. User-defined functions allow you to:
19
+ While UDFs provide flexibility, they come with significant drawbacks, primarily
20
+ related to performance. Unlike vectorized pandas operations, UDFs are slower because pandas lacks
21
+ insight into what they are computing, making it difficult to apply efficient handling or optimization
22
+ techniques. As a result, pandas resorts to less efficient processing methods that significantly
23
+ slow down computations. Additionally, relying on UDFs often sacrifices the benefits
24
+ of pandas’ built-in, optimized methods, limiting compatibility and overall performance.
21
25
22
- * **Customize Computations **: Implement logic tailored to your dataset, such as complex
23
- transformations, domain-specific calculations, or conditional modifications.
24
- * **Improve Code Readability **: Encapsulate logic into functions rather than writing long,
25
- complex expressions.
26
- * **Handle Complex Grouped Operations **: Perform operations on grouped data that standard
27
- methods do not support.
28
- * **Extend pandas' Functionality **: Apply external libraries or advanced calculations that
29
- are not natively available.
26
+ .. note ::
27
+ In general, most tasks can and should be accomplished using pandas’ built-in methods or vectorized operations.
28
+
29
+ Despite their drawbacks, UDFs can be helpful when:
30
30
31
+ * **Custom Computations Are Needed **: Implementing complex logic or domain-specific calculations that pandas'
32
+ built-in methods cannot handle.
33
+ * **Extending pandas' Functionality **: Applying external libraries or specialized algorithms unavailable in pandas.
34
+ * **Handling Complex Grouped Operations **: Performing operations on grouped data that standard methods do not support.
31
35
32
- What functions support User-Defined Functions
33
- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
36
+ Methods that support User-Defined Functions
37
+ -------------------------------------------
34
38
35
39
User-Defined Functions can be applied across various pandas methods:
36
40
@@ -47,124 +51,66 @@ User-Defined Functions can be applied across various pandas methods:
47
51
Series in a clean, readable manner.
48
52
49
53
All of these pandas methods can be used with both Series and DataFrame objects, providing versatile
50
- ways to apply user-defined functions across different pandas data structures.
51
-
54
+ ways to apply UDFs across different pandas data structures.
55
+
56
+
57
+ Choosing the Right Method
58
+ -------------------------
59
+ When applying UDFs in pandas, it is essential to select the appropriate method based
60
+ on your specific task. Each method has its strengths and is designed for different use
61
+ cases. Understanding the purpose and behavior of each method will help you make informed
62
+ decisions, ensuring more efficient and maintainable code.
63
+
64
+ Below is a table overview of all methods that accept UDFs:
65
+
66
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
67
+ | Method | Purpose | Supports UDFs | Keeps Shape | Performance | Recommended Use Case |
68
+ +==================+======================================+===========================+====================+===========================+==========================================+
69
+ | :meth: `apply ` | General-purpose function | Yes | Yes (when axis=1) | Slow | Custom row-wise or column-wise operations|
70
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
71
+ | :meth: `agg ` | Aggregation | Yes | No | Fast (if using built-ins) | Custom aggregation logic |
72
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
73
+ | :meth: `transform`| Transform without reducing dimensions| Yes | Yes | Fast (if vectorized) | Broadcast Element-wise transformations |
74
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
75
+ | :meth: `map ` | Element-wise mapping | Yes | Yes | Moderate | Simple element-wise transformations |
76
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
77
+ | :meth: `pipe ` | Functional chaining | Yes | Yes | Depends on function | Building clean pipelines |
78
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
79
+ | :meth: `filter ` | Row/Column selection | Not directly | Yes | Fast | Subsetting based on conditions |
80
+ +------------------+--------------------------------------+---------------------------+--------------------+---------------------------+------------------------------------------+
52
81
53
82
:meth: `DataFrame.apply `
54
- -----------------------
55
-
56
- The :meth: `DataFrame.apply ` allows applying a user-defined functions along either axis (rows or columns):
57
-
58
- .. ipython :: python
59
-
60
- import pandas as pd
83
+ ~~~~~~~~~~~~~~~~~~~~~~~
61
84
62
- # Sample DataFrame
63
- df = pd.DataFrame({' A' : [1 , 2 , 3 ], ' B' : [4 , 5 , 6 ]})
64
-
65
- # User-Defined Function
66
- def add_one (x ):
67
- return x + 1
68
-
69
- # Apply function
70
- df_applied = df.apply(add_one)
71
- print (df_applied)
72
-
73
- # This works with lambda functions too
74
- df_lambda = df.apply(lambda x : x + 1 )
75
- print (df_lambda)
85
+ The :meth: `DataFrame.apply ` allows you to apply UDFs along either rows or columns. While flexible,
86
+ it is slower than vectorized operations and should be used only when you need operations
87
+ that cannot be achieved with built-in pandas functions.
76
88
89
+ When to use: :meth: `DataFrame.apply ` is suitable when no alternative vectorized method is available, but consider
90
+ optimizing performance with vectorized operations wherever possible.
77
91
78
- :meth: `DataFrame.apply ` also accepts dictionaries of multiple user-defined functions:
79
-
80
- .. ipython :: python
81
-
82
- # Sample DataFrame
83
- df = pd.DataFrame({' A' : [1 , 2 , 3 ], ' B' : [1 , 2 , 3 ]})
84
-
85
- # User-Defined Function
86
- def add_one (x ):
87
- return x + 1
88
-
89
- def add_two (x ):
90
- return x + 2
91
-
92
- # Apply function
93
- df_applied = df.apply({" A" : add_one, " B" : add_two})
94
- print (df_applied)
95
-
96
- # This works with lambda functions too
97
- df_lambda = df.apply({" A" : lambda x : x + 1 , " B" : lambda x : x + 2 })
98
- print (df_lambda)
99
-
100
- :meth: `DataFrame.apply ` works with Series objects as well:
101
-
102
- .. ipython :: python
103
-
104
- # Sample Series
105
- s = pd.Series([1 , 2 , 3 ])
106
-
107
- # User-Defined Function
108
- def add_one (x ):
109
- return x + 1
110
-
111
- # Apply function
112
- s_applied = s.apply(add_one)
113
- print (s_applied)
114
-
115
- # This works with lambda functions too
116
- s_lambda = s.apply(lambda x : x + 1 )
117
- print (s_lambda)
92
+ Examples of usage can be found at :meth: `DataFrame.apply `
118
93
119
94
:meth: `DataFrame.agg `
120
- ---------------------
121
-
122
- The :meth: `DataFrame.agg ` allows aggregation with a user-defined function along either axis (rows or columns):
123
-
124
- .. ipython :: python
125
-
126
- # Sample DataFrame
127
- df = pd.DataFrame({
128
- ' Category' : [' A' , ' A' , ' B' , ' B' ],
129
- ' Values' : [10 , 20 , 30 , 40 ]
130
- })
95
+ ~~~~~~~~~~~~~~~~~~~~~
131
96
132
- # Define a function for group operations
133
- def group_mean (group ):
134
- return group.mean()
97
+ If you need to aggregate data, :meth: `DataFrame.agg ` is a better choice than apply because it is
98
+ specifically designed for aggregation operations.
135
99
136
- # Apply UDF to each group
137
- grouped_result = df.groupby(' Category' )[' Values' ].agg(group_mean)
138
- print (grouped_result)
100
+ When to use: Use :meth: `DataFrame.agg ` for performing aggregations like sum, mean, or custom aggregation
101
+ functions across groups.
139
102
140
- In terms of the API, :meth: `DataFrame.agg ` has similar usage to :meth: `DataFrame.apply `,
141
- but it is primarily used for **aggregation **, applying functions that summarize or reduce data.
142
- Typically, the result of :meth: `DataFrame.agg ` reduces the dimensions of data as shown
143
- in the above example. Conversely, :meth: `DataFrame.apply ` is more general and allows for both
144
- transformations and custom row-wise or element-wise operations.
103
+ Examples of usage can be found at :meth: `DataFrame.agg <api.dataframe.agg> `
145
104
146
105
:meth: `DataFrame.transform `
147
- ---------------------------
106
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~
148
107
149
- The :meth: ` DataFrame. transform` allows transforms a Dataframe, Series or Grouped object
150
- while preserving the original shape of the object .
108
+ The transform method is ideal for performing element-wise transformations while preserving the shape of the original DataFrame.
109
+ It’s generally faster than apply because it can take advantage of pandas' internal optimizations .
151
110
152
- .. ipython :: python
153
-
154
- # Sample DataFrame
155
- df = pd.DataFrame({' A' : [1 , 2 , 3 ], ' B' : [4 , 5 , 6 ]})
156
-
157
- # User-Defined Function
158
- def double (x ):
159
- return x * 2
111
+ When to use: When you need to perform element-wise transformations that retain the original structure of the DataFrame.
160
112
161
- # Apply transform
162
- df_transformed = df.transform(double)
163
- print (df_transformed)
164
-
165
- # This works with lambda functions too
166
- df_lambda = df.transform(lambda x : x * 2 )
167
- print (df_lambda)
113
+ Documentation: DataFrame.transform
168
114
169
115
Attempting to use common aggregation functions such as ``mean `` or ``sum `` will result in
170
116
values being broadcasted to the original dimensions:
@@ -187,15 +133,17 @@ values being broadcasted to the original dimensions:
187
133
print (df)
188
134
189
135
:meth: `DataFrame.filter `
190
- ------------------------
136
+ ~~~~~~~~~~~~~~~~~~~~~~~~
191
137
192
138
The :meth: `DataFrame.filter ` method is used to select subsets of the DataFrame’s
193
139
columns or row. It is useful when you want to extract specific columns or rows that
194
140
match particular conditions.
195
141
142
+ When to use: Use :meth: `DataFrame.filter ` when you want to use a UDF to create a subset of a DataFrame or Series
143
+
196
144
.. note ::
197
- :meth: `DataFrame.filter ` does not accept user-defined functions , but can accept
198
- list comprehensions that have user-defined functions applied to them.
145
+ :meth: `DataFrame.filter ` does not accept UDFs , but can accept
146
+ list comprehensions that have UDFs applied to them.
199
147
200
148
.. ipython :: python
201
149
@@ -207,72 +155,45 @@ match particular conditions.
207
155
' D' : [10 , 11 , 12 ]
208
156
})
209
157
158
+ # Define a function that filters out columns where the name is longer than 1 character
210
159
def is_long_name (column_name ):
211
160
return len (column_name) > 1
212
161
213
- # Define a function that filters out columns where the name is longer than 1 character
214
162
df_filtered = df[[col for col in df.columns if is_long_name(col)]]
215
163
print (df_filtered)
216
164
217
165
:meth: `DataFrame.map `
218
- ---------------------
219
-
220
- The :meth: `DataFrame.map ` method is used to apply a function element-wise to a pandas Series
221
- or Dataframe. It is particularly useful for substituting values or transforming data.
222
-
223
- .. ipython :: python
224
-
225
- # Sample DataFrame
226
- df = pd.DataFrame({ ' A' : [' cat' , ' dog' , ' bird' ], ' B' : [' pig' , ' cow' , ' lamb' ] })
166
+ ~~~~~~~~~~~~~~~~~~~~~
227
167
228
- # Using map with a user-defined function
229
- def animal_to_length (animal ):
230
- return len (animal)
168
+ :meth: `DataFrame.map ` is used specifically to apply element-wise UDFs and is better
169
+ for this purpose compared to :meth: `DataFrame.apply ` because of its better performance.
231
170
232
- df_mapped = df.map(animal_to_length)
233
- print (df_mapped)
171
+ When to use: Use map for applying element-wise UDFs to DataFrames or Series.
234
172
235
- # This works with lambda functions too
236
- df_lambda = df.map(lambda x : x.upper())
237
- print (df_lambda)
173
+ Documentation: DataFrame.map
238
174
239
175
:meth: `DataFrame.pipe `
240
- ----------------------
241
-
242
- The :meth: `DataFrame.pipe ` method allows you to apply a function or a series of functions to a
243
- DataFrame in a clean and readable way. This is especially useful for building data processing pipelines.
244
-
245
- .. ipython :: python
246
-
247
- # Sample DataFrame
248
- df = pd.DataFrame({ ' A' : [1 , 2 , 3 ], ' B' : [4 , 5 , 6 ] })
249
-
250
- # User-defined functions for transformation
251
- def add_one (df ):
252
- return df + 1
176
+ ~~~~~~~~~~~~~~~~~~~~~~
253
177
254
- def square ( df ):
255
- return df ** 2
178
+ The pipe method is useful for chaining operations together into a clean and readable pipeline.
179
+ It is a helpful tool for organizing complex data processing workflows.
256
180
257
- # Applying functions using pipe
258
- df_piped = df.pipe(add_one).pipe(square)
259
- print (df_piped)
181
+ When to use: Use pipe when you need to create a pipeline of transformations and want to keep the code readable and maintainable.
260
182
261
- The advantage of using :meth: `DataFrame.pipe ` is that it allows you to chain together functions
262
- without nested calls, promoting a cleaner and more readable code style.
183
+ Documentation: DataFrame.pipe
263
184
264
185
265
- Performance Considerations
266
- --------------------------
186
+ Best Practices
187
+ --------------
267
188
268
- While user-defined functions provide flexibility, their use is currently discouraged as they can introduce
189
+ While UDFs provide flexibility, their use is currently discouraged as they can introduce
269
190
performance issues, especially when written in pure Python. To improve efficiency,
270
- consider using built-in ``NumPy `` or ``pandas `` functions instead of user-defined functions
191
+ consider using built-in ``NumPy `` or ``pandas `` functions instead of UDFs
271
192
for common operations.
272
193
273
194
.. note ::
274
195
If performance is critical, explore **vectorizated operations ** before resorting
275
- to user-defined functions .
196
+ to UDFs .
276
197
277
198
Vectorized Operations
278
199
~~~~~~~~~~~~~~~~~~~~~
@@ -285,10 +206,10 @@ Below is an example of vectorized operations in pandas:
285
206
def calc_ratio(row):
286
207
return 100 * (row["one"] / row["two"])
287
208
288
- df["new_col2 "] = df.apply(calc_ratio, axis=1)
209
+ df["new_col "] = df.apply(calc_ratio, axis=1)
289
210
290
211
# Vectorized Operation
291
- df["new_col "] = 100 * (df["one"] / df["two"])
212
+ df["new_col2 "] = 100 * (df["one"] / df["two"])
292
213
293
214
Measuring how long each operation takes:
294
215
@@ -298,8 +219,8 @@ Measuring how long each operation takes:
298
219
User-defined function: 5.6435 secs
299
220
300
221
Vectorized operations in pandas are significantly faster than using :meth: `DataFrame.apply `
301
- with user-defined functions because they leverage highly optimized C functions
222
+ with UDFs because they leverage highly optimized C functions
302
223
via NumPy to process entire arrays at once. This approach avoids the overhead of looping
303
224
through rows in Python and making separate function calls for each row, which is slow and
304
225
inefficient. Additionally, NumPy arrays benefit from memory efficiency and CPU-level
305
- optimizations, making vectorized operations the preferred choice whenever possible.
226
+ optimizations, making vectorized operations the preferred choice whenever possible.
0 commit comments