Contents

Frequency Tables and Writing CSVs

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See the accompanied youtube video at the link here.

What is Frequency?

Before we explain what a frequency table is, you must know what frequency means first.

Frequency is simply put, the number of times a value occurs within the data. Let’s look at an example using our candybars dataset.

candybars_mini
name weight available_canada_america
0 Coffee Crisp 50 Canada
1 Butterfinger 184 America
2 Skor 39 Both
3 Smarties 45 Canada
4 Twix 58 Both
5 Reeses Peanutbutter Cups 43 Both
6 3 Musketeers 54 America

If we count the number of times the value Both appears in the available_canada_america column, we get 3 times. This is the frequency of the value both.

What is a Frequency Table?

A frequency table is a manner of displaying all the possible values of a column in our dataframe and the number of occurrences (frequencies) of each value.

For our sample data, a frequency table for the available_canada_america column would look like this:

Both       3
Canada     2
America    2
Name: available_canada_america, dtype: int64

If we want to get a frequency table of a categorical column, there are a few steps that need to be followed.

Up until now, we discussed getting a single column from a dataframe using double square brackets - df[['column name']].

For frequency tables, however, we only use single brackets to obtain the column values.

mfr_column = cereal['mfr']
mfr_column

0     N
1     Q
2     K
3     K
4     R
     ..
72    G
73    G
74    R
75    G
76    G
Name: mfr, Length: 77, dtype: object

We saved the object in this example here to an object named mfr_column in the same way that we have done this before.

Now we can use .value_counts() on this mfr_column variable to reference it, and we can obtain the frequency value for the different categories in that variable.

mfr_freq = mfr_column.value_counts()
mfr_freq

K    23
G    22
P     9
Q     8
R     8
N     6
A     1
Name: mfr, dtype: int64

If we did instead use double square brackets with pd.value_counts(), we would get an error. So it is important to take care and remember when you are using value_counts(), you only use one set of square brackets.

mfr_col_wrong = cereal[['mfr']]
mfr_col_wrong
mfr
0 N
1 Q
2 K
3 K
4 R
... ...
72 G
73 G
74 R
75 G
76 G

77 rows × 1 columns

mfr_col_wrong.value_counts()

mfr
K      23
G      22
P       9
Q       8
R       8
N       6
A       1
dtype: int64

Saving a dataframe

Sometimes it is useful to save a new dataframe to a file like a csv file for future use by you or somebody else.

We can do this using a method called .to_csv().

mfr_freq.to_csv('mfr_frequency.csv', index=False)

We put our desired csv file name in quotations within the parentheses and follow it with the argument index=False so we don’t export our index column which is just a column of numbers.