pandas-dp is a differential privacy extension for Pandas. Use it simply by importing the module and instantiating a mechanism. Use the .private accessor on any Dataframe or Series to access differentially private versions of algorithms, or obtain noise directly.
import numpy as np
import pandas as pd
# pandas-dp
import pandas_dp as pr
df = pd.DataFrame({"longitude": np.linspace(0, 10, num=5), "latitude": np.linspace(0, 20, num=5)})
# longitude latitude
# 0 0.0 0.0
# 1 2.5 5.0
# 2 5.0 10.0
# 3 7.5 15.0
# 4 10.0 20.0
df.longitude.mean()
# 5.0
mechanism = pr.LaplaceMechanism(epsilon=1)
df.longitude.private.mean(mechanism=mechanism)
# 4.861801498972586
# Each call is a new sample
df.longitude.private.mean(mechanism=mechanism)
# 4.927190360876554
# Noise an entire dataframe, without need to specify columns!
df_priv = df.private.noise(mechanism=mechanism)
# longitude latitude
# 0 2.284351 2.026169
# 1 3.645227 3.253937
# 2 6.068457 10.474715
# 3 8.633906 15.564795
# 4 10.120258 21.111389