Pandas is one of the most used libraries in Python for data science or data analysis. It can read data from CSV or Excel files, manipulate the data, and generate insights from it. Pandas can also be used to clean data, filter data, and visualize data.
Whether you are a beginner or an experienced professional, Pandas functions can help you to save time and effort when working with a dataset. In this article, we will provide a detail overview of the most important Pandas functions. We’ve also provide links to detailed articles that explain each function in more detail.
By the end of this article, you will have a solid understanding of the each functions of pandas in python that you need to know for Data Analysis as well as Data Science and you will be able to use these functions to load, clean, transform, and analyze data with ease.
List of Important Pandas Functions
Here are the list of some of the most important Pandas functions:
Function |
Description |
|---|---|
| Pandas read_csv() Function | This function is used to retrieve data from CSV files in the form of a dataframe. |
| Pandas head() Function | This function is used to return the top n (5 by default) values of a data frame or series. |
| Pandas tail() Function | This method is used to return the bottom n (5 by default) rows of a data frame or series. |
| Pandas sample() Function | This method is used to generate a sample random row or column from the data frame. |
| Pandas info() Function | This method is used to generate the summary of the DataFrame, this will include info about columns with their names, their datatypes, and missing values. |
| Pandas dtypes() Function | This method returns a Series with the data type of each column. |
| Pandas shape() Function | It returns a tuple representing the dimensionality of the Pandas DataFrame. |
| Pandas size() Function | This method returns the number of rows in the Series. Otherwise, return the number of rows times the number of columns in the DataFrame. |
| Pandas ndim() Function | This function returns 1 if Series and 2 if DataFrame |
| Pandas describe() Function | Returns descriptive statistics about the data like mean, minimum, maximum, standard deviation, etc. |
| Pandas unique() Function | It returns all the unique values in a particular column. |
| Pandas nunique() Function | Returns the number of unique values in the column |
| Pandas isnull() Function | Returns the DataFrame/Series of the boolean values. Missing values gets mapped to True and non-missing value gets mapped to False. |
| Python isna() Function |
Returns dataframe/series with bool values. Missing values gets mapped to True and non-missing gets mapped to False. |
| Pandas fillna() Function | This function is used to trim values at a specified input threshold. |
| Pandas clip() Function | Returns index information of the DataFrame. |
| Pandas columns() Function | Returns column names of the dataframe |
| Pandas sort_values() Function | This method sorts the data frame in ascending or descending order of passed Column. |
| Pandas value_counts() Function | Returns the counts of the unique values in a series or from a dataframe’s column |
| Pandas nlargest() Function | Used to get n largest values from a data frame or a series. |
| Pandas nsmallest() Function | Used to get n smallest values from a data frame or a series. |
| Pandas copy() Function | To copy DataFrame in Pandas. |
| Pandas loc() Function | Used to access a group of rows and columns by label(s) or a boolean array in the given dataframe. |
| Pandas iloc() Function | This method is used to retrieve rows from a dataframe. |
| Pandas rename() Function | This method is used to rename any index, column, or row. |
| Pandas where() Function | This method is used to check a data frame for one or more conditions and return the result accordingly. |
| Pandas drop() Function | Used to drop rows/columns from a dataframe. |
| Pandas groupby() Function | Used to group data based on some criteria. |
| Pandas corr() Function | This function is used to find the correlation among the columns in the Dataframe. |
| Pandas query() Function |
To filter dataframe based on a certain condition. |
| Pandas insert() Function | This method allows us to insert a column at any position. |
| Pandas sum() Function |
It returns the sum of the values for the requested axis. |
| Pandas mean() Function | It returns the mean of the values for the requested axis. |
| Pandas median() Function | It returns the median of the values for the requested axis. |
| Pandas std() Function | It returns sample standard deviation over the requested axis. |
| Pandas apply() Function | Using this we can apply a function to every row in the given dataframe. |
| Pandas merge() Function | Used to merge two Pandas dataframes. |
| Pandas astype() Function | This method is used to cast pandas object to a specified dtype. |
| Pandas set_index() Function | This method is used to set a List, Series or Data frame as an index of a Data Frame. |
| Pandas reset_index() Function | This method is used to reset the index of a Data Frame. |
| Pandas at() Function | This method is used to return data in a dataframe at the passed location. |
| Pandas iterrows() Function | This function is used to iterate over Pandas Dataframe rows in the form of (index, series) pair. |
| Pandas iteritems() Function | This function iterates over the given series object. |
| Pandas to_datetime() Function | This method helps to convert the string Date time into a Python Date time object. |
| Pandas to_numeric() Function | This method is used to convert an argument to a numeric type. |
| Pandas to_string() Function | This method is used to render the given DataFrame to a console-friendly tabular output. |
| Pandas concat() Function | This function is used to concatenate dataframes along a particular axis. |
| Pandas cov() Function | This method is used to compute the pairwise covariance of columns. |
| Pandas duplicated() Function | This method helps in analyzing duplicate values only. It returns a boolean series which is True only for Unique elements. |
| Pandas drop_duplicates() Function | This method removes the duplicates from Pandas’s dataframe. |
| Pandas dropna() Function | This method helps in dropping Rows/Columns with Null values |
| Pandas diff() Function | This method is used to find the first discrete difference of objects over the given axis. |
| Pandas rank() Function | This method returns a rank of every respective index of a series passed. The rank is returned on the basis of position after sorting. |
| Pandas mask() Function | |
| Pandas resample() Function | This method is used to resample the Time Series data. |
| Pandas transform() Function | This function calls a function on self-producing a DataFrame with transformed values that have the same axis length as self. |
| Pandas replace() Function | This function is used to replace values. |
| Pandas to_csv() Function | This function is used to write series/dataframe objects to comma-separated values (csv) files. |
| Pandas to_excel() Function | This method is used to export the DataFrame to the Excel file. |
| Pandas_to_sql() Function | This function is used to write the given dataframe to a SQL database. |
| Pandas plot() Function | This method is used to plot dataframe. |
Pandas Functions – FAQs
1. What are the most used Pandas functions for data analysis?
Some of the most used Pandas functions for data analysis include:
- `read_csv()`: Load data from a CSV file
- `fillna()`: Replace missing values in a DataFrame
- `mean()`: Calculate the mean of a Series or DataFrame
- `std()`: Calculate the standard deviation of a Series or DataFrame
- `describe()`: Calculate summary statistics for a Series or DataFrame
- `plot()`: Plot a Series or DataFrame
2. How do I import Pandas and access its functions?
To utilize Pandas functions, begin by importing the Pandas library using the standard convention: import pandas as pd. Once imported, you can access functions through the pd namespace, invoking them on data structures like DataFrames and Series.
3. How do I filter and clean data using Pandas functions?
To filter data, utilize functions like loc[] and iloc[] for label and index-based selection. Cleaning data involves functions like dropna(), fillna(), and replace(), which address missing values and incorrect entries.
