Python Melt function: a tool for data reshaping
In the process of data analysis, it is often encountered that the data needs to be reshaped from a wide table to a long table or vice versa. The melt function in Python provides a simple and fast way to solve this problem.
What is the Melt function?
The Melt function is a function in the pandas library that can reshape wide format data in a DataFrame into long format. Specifically, it is to convert some columns into rows to facilitate data analysis and visualization. The function is defined as follows:
DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)
The meanings of the parameters are as follows:
- id_vars: Columns that need to be reserved, that is, columns that do not need to be converted into rows, which can be column names or column indexes.
- value_vars: The columns that need to be converted into rows, which can be column names or column indexes.
- var_name: The name of the column that is converted into a row, which can be customized.
- value_name: The name of the new column, which can be customized.
- col_level: When multi-layer column indexing, which level is the specific operation.
How to use the Melt function?
Below we use a simple example to demonstrate the use of the Melt function. First, we construct a DataFrame containing course grades.
import pandas as pd
data = {<!-- -->
'Name': ['Xiaoming', 'Xiaogang', 'Xiaohong'],
'Chinese score': [86, 92, 88],
'Math score': [90, 89, 92],
'English score': [91, 86, 90]
}
df = pd. DataFrame(data)
print(df)
The output is as follows:
Name Chinese score Math score English score 0 Xiao Ming 86 90 91 1 Xiaogang 92 89 86 2 Little Red 88 92 90
Next, we use the melt function to convert the DataFrame into a long table.
df_melt = df.melt(id_vars=['name'], value_vars=['Chinese score', 'math score', 'English score'], var_name='subject\ ', value_name='grade') print(df_melt)
The output is as follows:
Name Subject Grade 0 Xiao Ming Chinese score 86 1 Xiaogang Chinese score 92 2 Xiaohong Chinese score 88 3 Xiao Ming's math score is 90 4 Xiaogang Mathematics score 89 5 Xiaohong Math score 92 6 Xiao Ming's English score is 91 7 Xiaogang English score 86 8 Xiaohong English score 90
It can be seen from the output results that the melt function reshaped the original DataFrame according to the name and subject, and the grade column was converted into rows.
Main functions realized by Melt function
In addition to converting wide format data into long format, the Melt function also has the following main functions.
1. Remove duplicate values
In the process of converting the data format, the problem of duplicate values is often encountered. At this time, the drop_duplicates parameter of the Melt function can be used to remove duplicate values.
df_melt_drop_dup = df_melt.drop_duplicates(subset=['name', 'subject']) print(df_melt_drop_dup)
The output is as follows:
Name Subject Grade 0 Xiao Ming Chinese score 86 1 Xiaogang Chinese score 92 2 Xiaohong Chinese score 88 3 Xiao Ming's math score is 90 4 Xiaogang Mathematics score 89 5 Xiaohong Math score 92 6 Xiao Ming's English score is 91 7 Xiaogang English score 86 8 Xiaohong English score 90
2. Multi-level index conversion
For multi-layer index data, we can use the Melt function to convert.
data_multi = {<!-- -->('Xiao Ming', 'Chinese'): 86,
('Xiaogang', 'Chinese'): 92,
('Xiaohong', 'Chinese'): 88,
('Xiao Ming', 'Mathematics'): 90,
('Xiaogang', 'Mathematics'): 89,
('Xiaohong', 'Mathematics'): 92,
('Xiao Ming', 'English'): 91,
('Xiao Gang', 'English'): 86,
('Xiaohong', 'English'): 90}
df_multi = pd.DataFrame(data_multi, index=[0]).T.reset_index()
df_multi.columns = ['name', 'subject', 'grade']
df_multi_melt = df_multi.melt(id_vars=['name', 'subject'], value_vars=['grade'], var_name='type', value_name='value')
print(df_multi_melt)
The output is as follows:
Name Subject Type Value 0 Xiao Ming Chinese score 86 1 Xiaogang Chinese score 92 2 Xiaohong Chinese score 88 3 Xiao Ming's math score is 90 4 Xiaogang Mathematics score 89 5 Xiaohong math grade 92 6 Xiao Ming English score 91 7 Xiaogang English score 86 8 Xiaohong English score 90
3. Multi-column conversion
In some data analysis scenarios, it may be necessary to convert multiple columns into rows at the same time. At this time, multiple column names can be passed in the value_vars parameter.
data_multi_columns = {<!-- -->
'A1': [0, 1, 2],
'A2': [3, 4, 5],
'B1': [6, 7, 8],
'B2': [9, 10, 11],
'C1': [12, 13, 14],
'C2': [15, 16, 17]
}
df_multi_columns = pd. DataFrame(data_multi_columns)
df_multi_columns_melt = df_multi_columns.melt(value_vars=['A1', 'A2', 'B1', 'B2', 'C1', 'C2'], var_name=' column name', value_name='value')
print(df_multi_columns_melt)
The output is as follows:
column name value 0 A1 0 1 A1 1 2 A1 2 3 A2 3 4 A2 4 5 A2 5 6 B1 6 7 B1 7 8 B1 8 9 B2 9 10 B2 10 11 B2 11 12 C1 12 13 C1 13 14 C1 14 15 C2 15 16 C2 16 17 C2 17
Conclusion
The Melt function is a very useful function in Python data analysis, which provides a way to convert a wide table into a long table or vice versa. In addition, it also supports a series of functions such as removing duplicate values, multi-level index data conversion, and multi-column conversion, which solves many problems faced in data analysis. When performing data analysis, mastering the use of the Melt function can improve the efficiency of data analysis and quickly build analysis reports.
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