Jean-Jacques Zieliński

The iPhone keyboard has some notorious quirks, but there’s one little “feature” you may have noticed. Every time you type “omw” it is automatically expanded to “On my way!” This can be pretty annoying, so let’s turn it off.

Talking over text does not lend itself well to conveying emotion. That’s why every small detail—punctuation, case, and emoji— is so important. There’s a huge difference in how “omw” and “On my way!” can be interpreted. So lets you give the control back to you.

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The iOS and iPadOS feature responsible for this is called “Text Replacement.” It allows you to create custom text shortcuts for phrases. The “omw” shortcut is actually the only one included by default, for some reason.

To get started, open the Settings app on your iPhone or iPad.

Go to “General.”

Now go to “Keyboard.”

Select “Text Replacement.”

You’ll see the “omw” shortcut listed. Tap “Edit” in the bottom left corner.

Tap the red minus icon next to “omw” to delete it.

Finally, tap “Delete” to confirm.

Here are the complete steps.

For example, let’s create the following NumPy array that contains only numeric data (i.e., integers):

Run the code in Python, and you’ll get the following NumPy array:

You can now convert the NumPy array to Pandas DataFrame using the following syntax:

You’ll now get a DataFrame with 3 columns:

What if you’d like to add an index to the DataFrame?

For instance, let’s add the following index to the DataFrame:

So here is the complete code to convert the array to a DataFrame with an index:

You’ll now see the index on the left side of the DataFrame:

Let’s now create a new NumPy array that will contain a mixture of strings and numeric data (where the dtype for this array will be set to object):

Here is the new array with an object dtype:

You can then use the following syntax to convert the NumPy array to a DataFrame:

Here is the new DataFrame:

Let’s check the data types of all the columns in the new DataFrame by adding df.dtypes to the code:

Currently, all the columns under the DataFrame are objects/strings:

What if you’d like to convert some of the columns in the DataFrame from objects/strings to integers?

For example, suppose that you’d like to convert the last 3 columns in the DataFrame to integers.

To achieve this goal, you can use astype(int) as captured below:

Using astype(int) will give you int32 for those 3 columns:

Alternatively, you can use apply(int) which will get you int64 for those last 3 columns:

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