To find the modes of the columns in a DataFrame, or the mode value of a Series in pandas, the easiest way is to use the pandas **mode()** function.

`df.mode()`

When working with data, many times we want to calculate summary statistics to understand our data better. One such statistic is the mode, or the value which occurs most for a given variable.

Finding the mode in a column, or the mode for all columns or rows in a DataFrame using pandas is easy. We can use the pandas **mode()** function to find the mode value of columns in a DataFrame.

The pandas **mode()** function works for both numeric and object dtypes.

Let’s say we have the following DataFrame.

```
df = pd.DataFrame({'Age': [43,23,43,49,71,37],
'Test_Score':[90,87,96,96,87,79]})
print(df)
# Output:
Age Test_Score
0 43 90
1 23 87
2 43 96
3 49 96
4 71 87
5 37 79
```

To get the modes for all columns, we can call the pandas **mode()** function.

```
print(df.mode())
# Output:
Age Test_Score
0 43.0 87
1 NaN 96
```

There is one mode for “Age” and two modes for “Test_Score”.

If we only want to get the mode of one column, we can do this using the pandas **mode()** function in the following Python code:

```
print(df["Test_Score"].mode())
# Output:
0 87
1 96
dtype: int64
```

## Find the Mode of a Column with Object dtype in pandas

The **mode()** function works for both numeric and object dtypes.

Let’s say I have the following pandas DataFrame:

```
Name Weight_Change Month
0 Jim -16.20 1
1 Sally 12.81 1
2 Bob -20.45 1
3 Sue 15.35 1
4 Jill -12.43 1
5 Larry -18.52 1
6 Pam -6.10 2
7 Sally -2.81 2
8 Rose 12.45 2
9 Pat -0.32 2
10 Jill -1.23 2
11 Larry -8.52 2
12 Jim 5.20 3
13 Rob 12.81 3
14 Bob -2.45 3
15 Herman 5.35 3
16 Jill -2.43 3
17 Billy -1.85 3
```

We can use the **mode()** function to see who appears in our DataFrame the most by calling it on the “Name” column.

```
print(df["Name"].mode())
#Output:
0 Jill
dtype: object
```

Hopefully this article has been helpful for you to understand how to find the mode of a Series or DataFrame in pandas.

## Leave a Reply