## 20 Pandas Tips and Tricks for Beginners## Introduction
Following are the 20 Pandas tips and tricks for beginners: ## 1. Read data from CSVThe Pandas method Read data from a CSV file into a DataFrame: This code can be run by reading data from a CSV file named ## 2. Display DataFrameThe initial few rows of a DataFrame are printed using the method df.head(). If no input is given then the pandas head() method returns the top n rows of a DataFrame, which equals 5. Display the first few rows of a DataFrame: This function retrieves the first five rows since no parameter has been supplied. ## 3. Select columnsSelect particular columns from a DataFrame: This code enables targeted analysis or actions on those particular columns without altering the original DataFrame by extracting the specified columns ## 4. Filter rowsFilter rows based on a condition: The code selects rows from DataFrame df that have a value greater than 0 in column 'column'. It allocates the rows from DataFrame df to filtered_data after choosing those whose values in the column called ## 5. Group by and AggregateData is grouped using Pandas Group by a column and perform aggregation: This code calculates the mean of ## 6. Sort DataFrameWithout naming a specific column, Pandas' sort_values() method arranges the DataFrame 'df' according to its values, by default, in ascending order. This function makes rearranging rows according to the values in every column easier. Sort DataFrame by one or more columns: This function takes in a DataFrame ## 7. Handle missing valuesThe DataFrame Handle missing values in DataFrame: ## 8. Pivot tableThe pandas Create a pivot table from DataFrame: ## 9. Date and Time operationsPandas can handle and analyze dates and times in a DataFrame by using the pd.to_datetime() method to convert data into datetime objects. It can parse a variety of date formats in addition to returning a DatetimeIndex or datetime objects. Convert string to datetime format and extract date/time components: Using pd.to_datetime(), this code changes the DateTime format of the DataFrame ## 10. Convert categorical to numericalIn pandas, the function used to convert categorical data into numerical data, specifically dummy variables is known as Convert categorical variables to numerical values using one-hot encoding: The resultant new DataFrame is named ## 11. Rolling window operationsBy creating a rolling window object with the rolling() method in Pandas, one may apply functions such as mean, sum and so on over a defined window size along a DataFrame or series axis. This makes rolling statistics computation easier for time-series or sequential data analysis. Perform rolling window calculations on DataFrame: This code calculates the rolling mean of the ## 12. Interpolate missing valuesThe Interpolate missing values in DataFrame: This code uses linear interpolation to replace missing values in DataFrame ## 13. String operationsPerform string operations on DataFrame columns: A new column named "new_column" is added to the DataFrame "df", and each value in it is the uppercase counterpart of the corresponding value in the "column". It uses Pandas' str.upper() function to transform the strings to uppercase. ## 14. SamplingThe Randomly sample rows from DataFrame: This code randomly selects 100 rows from DataFrame "df" and creates a new DataFrame "sampled_df". It then offers a portion of the original data for processing or analysis. ## 15. Apply custom aggregationPandas' Apply custom aggregation functions in groupby: A custom aggregation function called ## 16. Convert data typesUse Pandas' ## 17. RankingPandas' Rank rows in DataFrame: This code determines the rank of the values in the ## 18. Convert DataFrame to numpy arrayPandas' Convert DataFrame to a Numpy array: This code turns a DataFrame called ## 19. Datetime indexingUse Pandas' Set the datetime column as an index for time series analysis: This code effectively changes the index labels to match the values in the column by changing the DataFrame ## 20. Drop columnsThe Drop columns from DataFrame: This code deletes ## 21. Export data to CSVPandas' Export DataFrame to a CSV file: Without adding the index values as a distinct column, this code saves the DataFrame ## ConclusionGaining proficiency with |