- Comparing Rows Between Two Pandas DataFrames
- Using Hierarchical Indexes With Pandas
- Reshaping Pandas DataFrames
- Data Visualization With Seaborn and Pandas
- Parse Data from PDFs with Tabula and Pandas
- Lazy Pandas and Dask
- Automagically Turn JSON into Pandas DataFrames
- Connecting Pandas to a Database with SQLAlchemy
- Dropping Rows of Data Using Pandas
- Merge Sets of Data in Python Using Pandas
- Another 'Intro to Data Analysis in Python Using Pandas' Post
You've heard the cliché before: it is often cited that roughly %80~ of a data scientist's role is dedicated to cleaning data sets. I Personally haven't looked in to the papers or clinical trials which prove this number (that was a joke), but the idea holds true: in the data profession, we find ourselves doing away with blatantly corrupt or useless data. The simplistic approach is to discard such data entirely, thus here we are.
What constitutes 'filthy' data is project-specific, and at times borderline subjective. Occasionally, the offenders are more obvious: these might include chunks of data which are empty, poorly formatted, or simply irrelevant. While 'bad' data can occasionally be fixed or salvaged via transforms, in many cases it's best to do away with rows entirely to ensure that only the fittest survive.
Drop Empty Rows or Columns
If you're looking to drop rows (or columns) containing empty data, you're in luck: Pandas'
dropna() method is specifically for this.
dropna() is a simple one-liner which accepts a number of useful arguments:
Technically you could run
MyDataFrame.dropna() without any parameters, and this would default to dropping all rows where are completely empty. If thats all you needed, well, I guess you're done already. Otherwise, here are the parameters you can include:
- Axis: Specifies to drop by row or column.
- How: Accepts one of two possible values: any or all. This will either drop an axis which is completely empty (all), or an axis with even just a single empty cell (any).
- Thresh: Here's an interesting one: thresh accepts an integer, and will drop an axis only if that number threshold of empty cells is breached.
- Subset: Accepts an array of which axis' to consider, as opposed to considering all by default.
- Inplace: If you haven't come across
inplaceyet, learn this now: changes will NOT be made to the DataFrame you're touching unless this is set to
Pandas' .drop() Method
.drop() method is used to remove entire rows or columns based on their name. If we can see that our DataFrame contains extraneous information (perhaps for example, the HR team is storing a preferred_icecream_flavor in their master records), we can destroy the column (or row) outright.
drop() looks something like this:
We'll attempt to cover the usage of these parameters in plain English before inevitably falling into useless lingo which you have not yet learned.
- Axis: Similar to the above, setting the axis specifies if you're trying to drop rows or columns.
- Labels: May refer to either the name (string) of the target axis, or its index (int). Of course, whether this is referring to columns or rows in the DataFrame is dependent on the value of the axis parameter. Labels are always defined in the 0th axis of the target DataFrame, and may accept multiple values in the form of an array when dropping multiple rows/columns at once.
Drop by Column Position
Like lists, both rows and columns have numerical indexes:
Drop by Label
If we pass an array of strings to
.drop(), Pandas will interpret this as dropping columns which match the names we pass (
"C" in the example below):
- Index, Columns: An alternative method for specifying the same as the above. Accepts single or multiple values. Setting columns=labels is equivalent to labels, axis=1. index=0* is equivalent to *labels=0.
- Levels: Used in sets of data which contain multiple hierarchical levels, similar to that of nested arrays. A high-level few of Hierarchical indexing can be found here.
- Inplace: Again, drop methods are not carried out on the target DataFrame unless explicitly stated. The purpose of this is to presumably preserve the original set of data during ad hoc manipulation.This adheres to the Python style-guide which states that actions should not be performed on live sets of data unless explicitly stated. Here is a video of some guy describing this for some reason.
- Errors: Accepts either ignore or raise, with 'raise' set as default. When errors='ignore' is set, no errors will be thrown and existing labels are dropped.
It's common to run into datasets which contain duplicate rows, either as a result of dirty data or some preliminary work on the dataset. Pandas has a method specifically for purging these rows called
When we run
drop_duplicates() on a DataFrame without passing any arguments, Pandas will refer to dropping rows where all data across columns is exactly the same. Running this will keep one instance of the duplicated row, and remove all those after:
drop_duplicates() has a few options we can play with:
- Subset: Let's say we wanted to detect duplicates only in a certain row, or even number of rows. We can pass either a column name (string) or a collection of columns (list) via the subset attribute to perform duplicate checking only against the provided columns. Note: even though we're only using certain columns to determine duplicates, any detected duplicates will drop the entire row.
- Keep: If we find duplicates, how do we know which of the duplicates to keep? By default, Pandas will keep the first appearance of that row, and discard all others thereafter (
keep='first'). To keep the last, we would use
keep='last. If we just want to drop all duplicates, we use
- Inplace: Using
my_dataframe.drop_duplicates(inplace=True)is the same as our example above:
my_dataframe = my_dataframe.drop_duplicates()
Drop by Criteria
We can also remove rows or columns based on whichever criteria your little heart desires. For example, if you really hate people named Chad, you can drop all rows in your Customer database who have the name Chad. Screw Chad.
Unlike previous methods, the popular way of handling this is simply by saving your DataFrame over itself give a passed value. Here's how we'd get rid of Chad:
The syntax may seem a bit off-putting to newcomers (note the repetition of
my_dataframe 3 times). The format of
my_dataframe[CONDITION] simply returns a modified version of
my_dataframe, where only the data matching the given condition is affected.
Since we're purging this data altogether, stating
my_dataframe = my_dataframe[CONDITION] is an easy (albeit destructive) method for shedding data and moving on with our lives.