How do I group numerical data into groups from a dataframe to make a bar chart with matplotlib?

Question

I have a dataframe only containing peoples ages and a want to make a bar chart with matplotlib. In order to do this i want to organize the data from the age column into groups like 0-4, 5-9 etc. How do I go about making a bar chart by putting the ages into groups?

Could you share some of the data in your dataframe? See: https://stackoverflow.com/questions/20109391/how-to-make-good-reproducible-pandas-examples — BrokenBenchmark, May 04 '22 at 03:55

score 0 · Answer 1 · answered May 02 '22 at 03:34

You can firstly assign each age group a numerical value, make it a new column in your dataframe, and then use this to plot a seaborn countplot. I am using countplot instead of barplot because I assume that you want to plot the counts of each group on the graph, and for this countplot is the most efficient method. If not then let me know.

Creating random data (Run the code below to work on the exact same data as me) -

np.random.seed(42)
ages = [np.random.randint(0, 80) for _ in range(10000)]
df = pd.DataFrame(ages, columns = ["Age"])

Making age groups -

age_groups = [range(age, age + 5) for age in range(0, 80, 5)]

Assigning each age a group -

age_group = []

for age in ages:
  for i, group in enumerate(age_groups):
    if age in group:
      age_group.append(i)
      break

Making it a new column in the dataframe -

df["Age Group"] = age_group

Plotting a seaborn countplot -

import seaborn as sns

plt.figure(figsize = (20,5))
sns.countplot(x = "Age Group", data = df)
plt.show()

Output -

How do I group numerical data into groups from a dataframe to make a bar chart with matplotlib?

1 Answers1