Average time difference of one column over condition

I have a dataframe like this:

[{'Year': 2019, 'time_diff_new': 0, 'type1': 'Nan', 'Age': 861},
 {'Year': 2019, 'time_diff_new': 100, 'type1': 'Nan', 'Age': 861},
 {'Year': 2019, 'time_diff_new': 105, 'type1': 'Nan', 'Age': 861},
 {'Year': 2019, 'time_diff_new': 113, 'type1': 'Breaking', 'Age': 861},
 {'Year': 2019, 'time_diff_new': 127, 'type1': 'Breaking', 'Age': 861},
 {'Year': 2020, 'time_diff_new': 299, 'type1': 'Breaking', 'Age': 861},
 {'Year': 2020, 'time_diff_new': 462, 'type1': 'Nan', 'Age': 861},
 {'Year': 2021, 'time_diff_new': 601, 'type1': 'Breaking', 'Age': 861},
 {'Year': 2021, 'time_diff_new': 605, 'type1': 'Nan', 'Age': 861},
 {'Year': 2021, 'time_diff_new': 771, 'type1': 'Breaking', 'Age': 861},
 {'Year': 2021, 'time_diff_new': 855, 'type1': 'Nan', 'Age': 861},
 {'Year': 2021, 'time_diff_new': 861, 'type1': 'Nan', 'Age': 861}]

time_diff_new represents the days difference from the first date.

I want to calculate over Age column, I want to check how much time it takes in days for the next breaking change ( from type1 column). But for Age, I want it to consider ranges, not just every value in Age individually.

Is there any way this could be achieved? I haven't done something like this before, so I am not sure where to start. Any suggestions or ideas would be highly appreciated.

As you have mentioned in the comments that you yourself don't know what the expected output of your example DataFrame, it is very difficult to know whether what we might provide as a solution is what you are looking for. From what I understand, you are looking to find the mean differences between type1="Breaking" for different Age ranges.

A couple of options for what you might be looking for:

# only analysis on "Breaking"
df1 = df[df.type1.eq("Breaking")]

General grouping of ages, whether determined by yourself or not:

# using pandas cut
groups = pd.cut(df1.Age, bins=3)
# or using numpy
import numpy as np
smallest = np.min(df1.Age)
largest = np.max(df1.Age)
num_edges = 3
groups = np.digitize(df1.Age, np.linspace(smallest, largest, num_edges))
# note that you could also use your own groups here, e.g.
# groups = np.digitize(df1.Age, [0, 20, 50, 100, 400, 600, 1000])

# ensure sorted
df1.sort_values("time_diff_new", inplace=True)
# group by groups and return mean difference between "Breaking"
df1.groupby(groups, as_index=True)["time_diff_new"].diff().mean()
# only 1 group returned here for sample data, as data has "Age"=861 for all columns

Or a function that returns the mean for a specified age range:

def range_mean(df, min_age, max_age):
    # filter for values between ages only, and where type1 is "Breaking"
    df = df.loc[df.Age.between(min_age, max_age) & df.type1.eq("Breaking")]
    # ensure sorted
    df.sort_values("time_diff_new", inplace=True)
    # return the mean difference
    return df.time_diff_new.diff().mean()
    

range_mean(df, 860, 862)
# 164.5