Tag Archives: pandas

Efficient Date Parsing Techniques in Python Using Pandas

Working with date and time data is a common but crucial task in data analysis and processing. Python, with its powerful libraries like Pandas, provides versatile tools to handle such data efficiently. In this article, we’ll explore various methods to parse and convert date strings into datetime objects using Pandas, a popular data manipulation libraryContinue reading “Efficient Date Parsing Techniques in Python Using Pandas”

Pandas DataFrame groupby() and agg()

In Pandas, the groupby() and agg() methods are closely related as groupby() is used to group the data in a DataFrame based on one or more columns and then the agg() method is used to perform aggregation operations on those groups. After grouping the data using groupby(), you can use agg() to specify one orContinue reading “Pandas DataFrame groupby() and agg()”

Python Pandas Date Conversion: How to Handle #N/A, 1/0/1900, and Blank Dates in Excel

When working with dates in Excel, it’s common to encounter data inconsistencies such as #N/A, 1/0/1900, or blank values in the ‘date’ column. Converting such columns to datetime format can be challenging, and it may lead to errors or unexpected results. For example, in the code provided below, an error occurs when attempting to convertContinue reading “Python Pandas Date Conversion: How to Handle #N/A, 1/0/1900, and Blank Dates in Excel”

Split a String Column into Multiple Columns in Pandas DataFrame with Regex

To use the .str.split() function in pandas DataFrame, you can refer to this link: But in this article, we’ll use regex to split the sting values and change them to multiple columns. Let’s see the sample: Example: Script: Output: Summary The best part of using regex when you want to split a column into multipleContinue reading “Split a String Column into Multiple Columns in Pandas DataFrame with Regex”

VLOOKUP in Pandas DataFrame

To do VLOOKUP like in excel, python’s built-in class DataFrame has a very detailed and handy method by using map() and merge() functions to merge two different data tables. The merge() function does the same job as the join in SQL and uses the left join to emulate the VLOOKUP function like in Excel (IfContinue reading “VLOOKUP in Pandas DataFrame”

TypeError: Sequence item 1: expected str instance, float found

When using agg() and join() methods on pandas DataFrame, sometimes you will get two below errors: , and: These errors may be due to the NaN value in your columns. Let’s try to create these errors with the below script: You will find below errors: 1. Future Warning: 2. Type Error: Let’s fill NaN withContinue reading “TypeError: Sequence item 1: expected str instance, float found”

Concatenate Column Values in Pandas DataFrame

Sometimes you need to concatenate column values in your Pandas DataFrame to create a new unique column (uid = unique id), so you can do a look-up value from another DataFrame. From the below example you will have 4 original columns: MRBTS, LNBTS, LNCEL, and dlChBw, and then add a new column ‘UID’ by concatenatingContinue reading “Concatenate Column Values in Pandas DataFrame”

Rename Column Name in Pandas DataFrame

You can change one or more column names of your pandas DataFrame with rename() method. Let’s see the sample below: Sample: On the Second script above, on the df.rename() method, inplace = True, to update in current DataFrame. And on the Third script, regex = True to prevent from warning below: Output:

Split String Column into Multiple Columns in Pandas DataFrame

You can use split() method to split a pandas DataFrame text column into multiple columns. Syntax: Parameters: Returns: The split() method returns a list of strings. The example below will split A column into two columns X and Y by expanding DataFrame dimensionality: Example: Output: