Pandas `DataFrame.compare()`

function is used to compare given DataFrames row by row along with the specified align_axis. Sometimes we have two or more DataFrames having the same data with slight changes, in those situations we need to observe the difference between two DataFrames. By default, `compare()`

function compares two DataFrames column-wise and returns the differences side by side. It can compare only DataFrames having the same shape with the same dimensions and having the same row indexes and column labels.

In this article, I will explain using `compare()`

function, its syntax, and parameters how we can compare the two DataFrames row by row with examples.

## 1. Quick Examples of Compare Two DataFrames Row by Row

If you are in a hurry, below are some quick examples of comparing two DataFrames row by row.

```
# Below are quick examples
# Example 1: Compare two DataFrames row by row
diff = df.compare(df1, align_axis = 0)
# Example 2: To ignore NaN values set keep_equal=True
diff = df.compare(df1, keep_equal=True, align_axis = 0)
# Example 3: Set keep_shape = true and keep same shape
diff = df.compare(df1, keep_shape = True, align_axis = 0)
# Example 4: Get differences of DataFrames keep equal values and shape
diff = df.compare(df1, keep_equal=True, keep_shape = True, align_axis = 0)
```

## 2. Syntax of Pandas df.compare()

Following is the syntax of the Pandas compare() function.

```
# Following is the syntax of compare() function
DataFrame.compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))
```

### 2.1 Parameters

Following are the parameters of the `compare()`

function.

`Other:`

It is a DataFrame Object and is used to compare with a given DataFrame.`align_axis:`

It defines the axis of comparison. The default value is`1`

for columns. If it is set with`0`

for rows. For columns resulting differences are merged vertically whereas, for rows resulting differences are merged horizontally.`keep_shape:`

(bool), Default value is`False`

. If it is`True`

, all rows and columns exist along with different values. Otherwise, only different values exist.`keep_equal :`

(bool) Default value is`False`

. If it is`True`

, keep all equal values instead of NaN values.`result_names`

: (tuple): Default (‘self’, ‘other’)

### 2.2 Return Value

It returns DataFrame where the elements are differences of given DataFrames. Resulting in DataFrame having a multi-index with ‘self’ and ‘other’ are at the innermost level of the row index.

**Create DataFrame**

Now, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are `Courses`

, `Fee`

, `Duration`

and `Discount`

.

```
# Create DataFrame
import pandas as pd
import pandas as pd
technologies = ({
'Courses':["Spark", "NumPY", "pandas", "Java", "PySpark"],
'Fee' :[20000,25000,30000,22000,26000],
'Duration':['30days','40days','35days','60days','50days'],
'Discount':[1000,2500,1500,1200,3000]
})
technologies1 = ({
'Courses':["Spark", "Hadoop", "pandas", "Java", "PySpark"],
'Fee' :[20000,24000,30000,22000,21000],
'Duration':['30days','40days','35days','60days','50days'],
'Discount':[1000,2500,1500,1200,3000]
})
df = pd.DataFrame(technologies)
print("DataFrame1:\n", df)
df1 = pd.DataFrame(technologies1)
print("DataFrame2:\n", df1)
```

Yields below output.

## 3. Usage of Pandas DataFrame.compare() Function.

Pandas DataFrame.compare() function compares two equal sizes and dimensions of DataFrames row by row along with align_axis = 0 and returns The DataFrame with unequal values of given DataFrames. By default, it compares the DataFrames column by column. If we want to get the same sized resulting DataFrame we can use its parameter keep_shape and use keep_equal param to avoid NaN values in the resulting DataFrame.

Let’s use this function on given DataFrames along with `align_axis=0`

to find the difference between two DataFrames row by row.

```
# Comparing the two DataFrames row by row
diff = df.compare(df1, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)
```

Yields below output.

As we can see from the above, differences have been added one by one in the resultant DataFrame.

## 4. Pass keep_equal into compare() & Compare

As we can see from the above, the resulting DataFrame has been obtained where equal values are treated as NaN values. So, overcome the NaN values by setting `keep_equal`

as `True`

then and pass into compare() function. It will override the NaN values with equal values of given DataFrames.

```
# Ignore NaN values pass keep_equal=True
diff = df.compare(df1, keep_equal=True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)
```

Yields below output.

```
# Output:
# After comparing two DataFrames:
Courses Fee
1 self NumPy 25000
other Hadoop 24000
4 self Pyspark 26000
other Pyspark 21000
```

## 5. Pass keep_shape into compare() & Compare Pandas Row by Row

If we want to get the same-sized resulting DataFrame, we can set `keep_shape`

as `True`

and then pass it to the `compare()`

function. It will return the same-sized DataFrame where equal values are treated as NaN values. For example,

```
# Set keep_shape = true and keep same shape
diff = df.compare(df1, keep_shape = True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)
```

Yields below output.

```
# Output:
# After comparing two DataFrames:
Courses Fee Duration Discount
0 self NaN NaN NaN NaN
other NaN NaN NaN NaN
1 self NumPy 25000.0 NaN NaN
other Hadoop 24000.0 NaN NaN
2 self NaN NaN NaN NaN
other NaN NaN NaN NaN
3 self NaN NaN NaN NaN
other NaN NaN NaN NaN
4 self NaN 26000.0 NaN NaN
other NaN 21000.0 NaN NaN
```

## 6. Pass keep_equal & keep_shape into compare()

Set `keep_shape`

and `keep_equal`

as `True`

and pass them into the compare() function it will return the same-sized resulting DataFrame along with equal values of given DataFrames.

```
# Get differences of DataFrames keep equal values and shape
diff = df.compare(df1, keep_equal=True, keep_shape = True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)
```

Yields below output.

```
# Output:
# After comparing two DataFrames:
Courses Fee Duration Discount
0 self Spark 20000 30days 1000
other Spark 20000 30days 1000
1 self NumPy 25000 40days 2500
other Hadoop 24000 40days 2500
2 self pandas 30000 35days 1500
other pandas 30000 35days 1500
3 self Java 22000 60days 1200
other Java 22000 60days 1200
4 self Pyspark 26000 50days 3000
other Pyspark 21000 50days 3000
```

## 7. Conclusion

In this article, I have explained `DataFrame`

.`compare()`

function and using its syntax, and parameters how we can compare the two DataFrames row by row along with multiple examples

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