I. Comparison operators and comparison methods
Comparison operators are used to determine equality and to compare sizes, and the comparison operators in Python are ==, ! =, <, >, <=, >= six, the same in Pandas.
In Pandas, DataFrame and Series also support six comparison methods, as detailed in the following table.
| methodologies | Full name in English | use |
| eq | equal to | be tantamount to |
| ne | not equal to | not equal ≠ |
| lt | less than | less than |
| gt | greater than | more than |
| le | less than or equal to | less than or equal to |
| ge | greater than or equal to | greater than or equal to |
For comparison operations, == and ! == support various types of data to compare with each other, while <, >, <=, >= have restrictions on data types, such as integers can be compared in size with floats, but integers can't be compared in size with strings, and an error will be reported. This applies to all comparisons that follow.
II. Comparison of two DataFrames
1. Comparison with arithmetic operators
Two DataFrames are compared by comparing the data in the corresponding positions in the DataFrame.
To use the comparison operator, both DataFrames must have the same shape and the indexes must be equal (the index order must be the same), otherwise an error will be reported.
2. Comparison by comparison
Call the compare method directly with a DataFrame and pass in another DataFrame to complete the compare operation.
When using the comparison method, the two DataFrames can have different shapes and different indexes. The result is a new DataFrame that is compatible with the two DataFrames being compared, in the following principle.
III. Comparison of the two Series
1. Comparison with arithmetic operators
To use the comparison operator, both Series must have the same length and the indexes must be equal (index order must be the same), otherwise an error will be reported.
2. Comparison by comparison
Using the comparison method, the lengths of the two Series can be different, and the indexes can also be different. The result is a new Series that is compatible with the two compared Series, the same principle as DataFrame.
IV. Comparison with numbers or strings
1. DataFrame and numerical comparison
Each data in a DataFrame is compared with a number to return a Boolean value at the corresponding position, and the same is true for Series. Comparison methods and operators work the same way.
2. DataFrame and string comparison
Compares each piece of data to the specified string, same for Series. Comparison methods and operators work the same way.
When comparing with multidimensional data with single data, pay attention to the type of data, if there is an unsupported comparison, an error will be reported.
V. Comparison with array
The compare operation also supports comparing DataFrame or Series with array data in numpy. array does not have an index, so there is no requirement for an index, but the shapes must be the same or an error will be reported. Comparison methods and operators work the same way.
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