From 0d9f4c2808202409b7710d9cdbc8bf533593cf91 Mon Sep 17 00:00:00 2001
From: acdm123 <39374111+acdm123@users.noreply.github.com>
Date: Sun, 26 Sep 2021 14:43:13 +0800
Subject: [PATCH] Update customer_churn.py
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新版pandas中已不使用ix函数，用loc和iloc替代了
---
 statistics/customer_churn.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/statistics/customer_churn.py b/statistics/customer_churn.py
index b5acc27..2d16bbd 100755
--- a/statistics/customer_churn.py
+++ b/statistics/customer_churn.py
@@ -113,7 +113,7 @@ def inverse_logit(model_formula):
 print("Probability of churn when account length changes by 1: %.2f" % (inverse_logit(cust_serv_mean) - inverse_logit(cust_serv_mean_minus_one)))
 
 # Predict churn for "new" observations
-new_observations = churn.ix[churn.index.isin(xrange(10)), independent_variables.columns]
+new_observations = churn.loc[churn.index.isin(range(10)), independent_variables.columns]
 new_observations_with_constant = sm.add_constant(new_observations, prepend=True)
 y_predicted = logit_model.predict(new_observations_with_constant)
 y_predicted_rounded = [round(score, 2) for score in y_predicted]
@@ -135,4 +135,4 @@ def inverse_logit(model_formula):
 # Predict output value for a new observation based on its mean standardized input values
 input_variables = [0., 0., 0., 1.]
 predicted_value = logit_model.predict(input_variables)
-print("Predicted value: %.5f") % predicted_value
\ No newline at end of file
+print("Predicted value: %.5f") % predicted_value