> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # How to Get most frequent values in Pandas DataFrame - URL: https://datascientyst.com/get-most-frequent-values-pandas-dataframe/ - Published: 2021-09-01T10:18:30.000Z - Updated: 2022-03-14T21:40:57.000Z - Author: John D K - Tags: count() In this short post, I'll show you how to **get most frequent values in Pandas DataFrame.** You can find also: [How to Get Top 10 Highest or Lowest Values in Pandas](https://datascientyst.com/get-top-10-highest-lowest-values-pandas/) To start, here are the ways **to get most frequent N values in your DataFrame**: ```python df['Magnitude'].value_counts() df['Magnitude'].mode() ``` In the next steps we will cover more details in simple examples ## Step 1: Create Sample DataFrame To start, let's create DataFrame with data from Kaggle:[Significant Earthquakes, 1965-2016](https://www.kaggle.com/usgs/earthquake-database?select=database.csv&ref=datascientyst.com). To learn more about Pandas and Kaggle: [How to Search and Download Kaggle Dataset to Pandas DataFrame](https://datascientyst.com/search-download-kaggle-dataset-pandas-dataframe/) ```python import pandas as pd cols = ['Date', 'Time', 'Latitude', 'Longitude', 'Depth', 'Magnitude Type', 'Type', 'ID', 'Magnitude'] df = pd.read_csv(f'../data/earthquakes_1965_2016_database.csv.zip')[cols] ``` data: | Date | Latitude | Longitude | Depth | Magnitude Type | Magnitude | | ---------- | -------- | --------- | ----- | -------------- | --------- | | 01/02/1965 | 19.246 | 145.616 | 131.6 | MW | 6.0 | | 01/04/1965 | 1.863 | 127.352 | 80.0 | MW | 5.8 | | 01/05/1965 | \-20.579 | \-173.972 | 20.0 | MW | 6.2 | | 01/08/1965 | \-59.076 | \-23.557 | 15.0 | MW | 5.8 | | 01/09/1965 | 11.938 | 126.427 | 15.0 | MW | 5.8 | ## Step 2: Get Most Frequent value of Column in Pandas To get the most frequent value of a column we can use the method `mode`. It will return the value that appears most often. It can be multiple values. So to get the most frequent value in a single column - 'Magnitude' we can use: ```python df['Magnitude'].mode() ``` result: ``` 0 5.5 dtype: float64 ``` It has few parameters like: - `numeric_only` - `dropna` - `axis` If the **most frequent values are multiple they will be returned**: ```python df['Time'].mode() ``` result: ``` 0 02:56:58 1 14:09:03 dtype: object ``` ## Step 3: Get Most Frequent value for all columns in Pandas In order **to get the most frequent value for all columns we can iterate through all columns of the DataFrame**: ```python dfs = [] for col in df.columns: top_values = [] top_values = df[col].mode() dfs.append(pd.DataFrame({col: top_values}).reset_index(drop=True)) pd.concat(dfs, axis=1) ``` the most frequent values for all columns: | Date | Time | Depth | Magnitude Type | Type | Magnitude | | ---------- | -------- | ----- | -------------- | ---------- | --------- | | 03/11/2011 | 02:56:58 | 10.0 | MW | Earthquake | 5.5 | | NaN | 14:09:03 | NaN | NaN | NaN | NaN | Or get the most frequent values per dtype-s - for example only numeric columns: ```python from pandas.api.types import is_numeric_dtype dfs = [] for col in df.columns: top_values = [] if is_numeric_dtype(df[col]): top_values = df[col].mode() dfs.append(pd.DataFrame({col: top_values}).reset_index(drop=True)) pd.concat(dfs, axis=1) ``` The most frequent values for numeric columns | Depth | Magnitude | | ----- | --------- | | 10.0 | 5.5 | ## Step 4: Get N most frequent values in a column To get 5, 10 or N most frequent values in a single column we can use method `value_counts`: ```python df['Magnitude'].value_counts() ``` it will return the frequency for all values in the column: ``` 5.50 4685 5.60 3967 5.70 3079 5.80 2346 5.90 1947 6.00 1580 .... Name: Magnitude, dtype: int64 ``` To get the **N most frequent values only without the count** we can use: ```python n = 5 df['Magnitude'].value_counts().index.tolist()[:n] ``` result: ``` [5.5, 5.6, 5.7, 5.8, 5.9] ``` To get only the count: ```python n = 5 df['Magnitude'].value_counts().values.tolist()[:n] ``` result: \[4685, 3967, 3079, 2346, 1947\] To get the **single most frequent value from `value_counts` we can combine it with `idmax`**: ```python df['Magnitude'].value_counts().idxmax() ``` output: ``` 5.5 ``` **To get the count of the most frequent value:** ```python df['Magnitude'].value_counts().max() ``` output: ``` 4685 ``` ## Step 5: Get Top 10 most frequent values for all columns Finally let's cover the case - how to get the most frequent values for all columns in DataFrame. For this purpose we are going to use loop: ```python from pandas.api.types import is_categorical_dtype for col in df.columns: print(col, end=' - \n') print('_' * 50) if col in ['Magnitude'] or is_categorical_dtype(col): display(pd.DataFrame(df[col].astype('str').value_counts().sort_values(ascending=False).head(3))) else: display(pd.DataFrame(df[col].value_counts().sort_values(ascending=False).head(5))) ``` In the example above we are applying different behaviour for categorical columns. Because value\_counts can't be applied directly on them. result: Date - --- Date 03/11/2011 128 12/26/2004 51 02/27/2010 39 Time - --- Time 02:56:58 5 14:09:03 5 16:25:34 4 ## Resources - [Notebook](https://github.com/softhints/datascientyst/blob/master/0.guides/get-most-frequent-values-pandas-dataframe.ipynb?ref=datascientyst.com) - [pandas.DataFrame.mode](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html?ref=datascientyst.com) - [pandas.DataFrame.value\_counts](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value%5Fcounts.html?ref=datascientyst.com) - [List of Aggregation Functions(aggfunc) for GroupBy in Pandas](https://datascientyst.com/list-aggregation-functions-aggfunc-groupby-pandas/)