# Forcing Lower Precision

**00:00**
Force lower precision. If you’re okay with less precise data types, then you can potentially save a significant amount of memory. First, get the data types with `.dtypes`

again.

**00:28**
The columns with the floating-point numbers are 64-bit floats, so each number of this `float64`

type consumes 64 bits, which is 8 bytes. Each column has 20 numbers, and therefore requires 160 bytes.

**00:42**
You can verify this with `.memory_usage()`

. `.memory_usage()`

returns an instance of `Series`

with the memory usage of each column in bytes.

**00:55**
You can conveniently combine it with `.loc[]`

and `.sum()`

to get the memory for a group of columns.

**01:05**
This example shows how you can combine the numeric columns `'POP'`

, `'AREA'`

, and `'GDP'`

to get their total memory requirement.

**01:14**
The argument `index=False`

excludes data for row labels from the resulting `Series`

object. For these three columns, you’ll need 480 bytes.

**01:26**
You can also extract the data values in the form of a NumPy array with `.to_numpy()`

or `.values`

. Then, use the `.nbytes`

attribute to get the total bytes consumed by the items of the array.

**01:40**
The result is the same 480 bytes. So, how do you save memory? In this case, you can specify that your numeric columns `'POP'`

, `'AREA'`

, and `'GDP'`

should have the type `float32`

.

**01:54**
Use the optional parameter `dtype`

to do this.

**02:24**
You can now verify that each numeric column needs 80 bytes, or 4 bytes per item.

**02:37**
Each value is a floating-point number of 32 bits or 4 bytes. The three numeric columns contain 20 items each. In total, you’ll need 240 bytes of memory when you work with the type `float32`

.

**02:52**
This is half the size of the 480 bytes you’d need with `float64`

numbers. In addition to saving memory, you can significantly reduce the time required to process data by using `float32`

instead of `float64`

in some cases.

**03:08**
Next up, using chunks to deal with your data in segments.

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