Merge pull request #1831 from redis/DOC-5225-testable-python-prob-examples

DOC-5225 enabled testable Python prob examples

Author: andy-stark-redis

content/develop/clients/redis-py/prob.md

@@ -99,47 +99,16 @@ add. The following example adds some names to a Bloom filter representing
 a list of users and checks for the presence or absence of users in the list.
 Note that you must use the `bf()` method to access the Bloom filter commands.
 
-```py
-res1 = r.bf().madd("recorded_users", "andy", "cameron", "david", "michelle")
-print(res1)  # >>> [1, 1, 1, 1]
-
-res2 = r.bf().exists("recorded_users", "cameron")
-print(res2)  # >>> 1
-
-res3 = r.bf().exists("recorded_users", "kaitlyn")
-print(res3)  # >>> 0
-```
-
-<!-- < clients-example home_prob_dts bloom Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts bloom Python >}}
+{{< /clients-example >}}
 
 A Cuckoo filter has similar features to a Bloom filter, but also supports
 a deletion operation to remove hashes from a set, as shown in the example
 below. Note that you must use the `cf()` method to access the Cuckoo filter
 commands.
 
-```py
-res4 = r.cf().add("other_users", "paolo")
-print(res4)  # >>> 1
-
-res5 = r.cf().add("other_users", "kaitlyn")
-print(res5)  # >>> 1
-
-res6 = r.cf().add("other_users", "rachel")
-print(res6)  # >>> 1
-
-res7 = r.cf().mexists("other_users", "paolo", "rachel", "andy")
-print(res7)  # >>> [1, 1, 0]
-
-res8 = r.cf().delete("other_users", "paolo")
-print(res8)  # >>> 1
-
-res9 = r.cf().exists("other_users", "paolo")
-print(res9)  # >>> 0
-```
-
-<!-- < clients-example home_prob_dts cuckoo Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts cuckoo Python >}}
+{{< /clients-example >}}
 
 Which of these two data types you choose depends on your use case.
 Bloom filters are generally faster than Cuckoo filters when adding new items,
@@ -159,28 +128,8 @@ You can also merge two or more HyperLogLogs to find the cardinality of the
 [union](https://en.wikipedia.org/wiki/Union_(set_theory)) of the sets they
 represent.
 
-```py
-res10 = r.pfadd("group:1", "andy", "cameron", "david")
-print(res10)  # >>> 1
-
-res11 = r.pfcount("group:1")
-print(res11)  # >>> 3
-
-res12 = r.pfadd("group:2", "kaitlyn", "michelle", "paolo", "rachel")
-print(res12)  # >>> 1
-
-res13 = r.pfcount("group:2")
-print(res13)  # >>> 4
-
-res14 = r.pfmerge("both_groups", "group:1", "group:2")
-print(res14)  # >>> True
-
-res15 = r.pfcount("both_groups")
-print(res15)  # >>> 7
-```
-
-<!-- < clients-example home_prob_dts hyperloglog Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts hyperloglog Python >}}
+{{< /clients-example >}}
 
 The main benefit that HyperLogLogs offer is their very low
 memory usage. They can count up to 2^64 items with less than
@@ -220,36 +169,8 @@ a Count-min sketch object, add data to it, and then query it.
 Note that you must use the `cms()` method to access the Count-min
 sketch commands.
 
-```py
-# Specify that you want to keep the counts within 0.01
-# (1%) of the true value with a 0.005 (0.5%) chance
-# of going outside this limit.
-res16 = r.cms().initbyprob("items_sold", 0.01, 0.005)
-print(res16)  # >>> True
-
-# The parameters for `incrby()` are two lists. The count
-# for each item in the first list is incremented by the
-# value at the same index in the second list.
-res17 = r.cms().incrby(
-    "items_sold",
-    ["bread", "tea", "coffee", "beer"],  # Items sold
-    [300, 200, 200, 100]
-)
-print(res17)  # >>> [300, 200, 200, 100]
-
-res18 = r.cms().incrby(
-    "items_sold",
-    ["bread", "coffee"],
-    [100, 150]
-)
-print(res18)  # >>> [400, 350]
-
-res19 = r.cms().query("items_sold", "bread", "tea", "coffee", "beer")
-print(res19)  # >>> [400, 200, 350, 100]
-```
-
-<!-- < clients-example home_prob_dts cms Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts cms Python >}}
+{{< /clients-example >}}
 
 The advantage of using a CMS over keeping an exact count with a
 [sorted set]({{< relref "/develop/data-types/sorted-sets" >}})
@@ -281,53 +202,8 @@ shows how to merge two or more t-digest objects to query the combined
 data set. Note that you must use the `tdigest()` method to access the
 t-digest commands.
 
-```py
-res20 = r.tdigest().create("male_heights")
-print(res20)  # >>> True
-
-res21 = r.tdigest().add(
-    "male_heights",
-    [175.5, 181, 160.8, 152, 177, 196, 164]
-)
-print(res21)  # >>> OK
-
-res22 = r.tdigest().min("male_heights")
-print(res22)  # >>> 152.0
-
-res23 = r.tdigest().max("male_heights")
-print(res23)  # >>> 196.0
-
-res24 = r.tdigest().quantile("male_heights", 0.75)
-print(res24)  # >>> 181
-
-# Note that the CDF value for 181 is not exactly
-# 0.75. Both values are estimates.
-res25 = r.tdigest().cdf("male_heights", 181)
-print(res25)  # >>> [0.7857142857142857]
-
-res26 = r.tdigest().create("female_heights")
-print(res26)  # >>> True
-
-res27 = r.tdigest().add(
-    "female_heights",
-    [155.5, 161, 168.5, 170, 157.5, 163, 171]
-)
-print(res27)  # >>> OK
-
-res28 = r.tdigest().quantile("female_heights", 0.75)
-print(res28)  # >>> [170]
-
-res29 = r.tdigest().merge(
-    "all_heights", 2, "male_heights", "female_heights"
-)
-print(res29)  # >>> OK
-
-res30 = r.tdigest().quantile("all_heights", 0.75)
-print(res30)  # >>> [175.5]
-```
-
-<!-- < clients-example home_prob_dts tdigest Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts tdigest Python >}}
+{{< /clients-example >}}
 
 A t-digest object also supports several other related commands, such
 as querying by rank. See the
@@ -349,48 +225,5 @@ top *k* items and query whether or not a given item is in the
 list. Note that you must use the `topk()` method to access the
 Top-K commands.
 
-```py
-# The `reserve()` method creates the Top-K object with
-# the given key. The parameters are the number of items
-# in the ranking and values for `width`, `depth`, and
-# `decay`, described in the Top-K reference page.
-res31 = r.topk().reserve("top_3_songs", 3, 7, 8, 0.9)
-print(res31)  # >>> True
-
-# The parameters for `incrby()` are two lists. The count
-# for each item in the first list is incremented by the
-# value at the same index in the second list.
-res32 = r.topk().incrby(
-    "top_3_songs",
-    [
-        "Starfish Trooper",
-        "Only one more time",
-        "Rock me, Handel",
-        "How will anyone know?",
-        "Average lover",
-        "Road to everywhere"
-    ],
-    [
-        3000,
-        1850,
-        1325,
-        3890,
-        4098,
-        770
-    ]
-)
-print(res32)
-# >>> [None, None, None, 'Rock me, Handel', 'Only one more time', None]
-
-res33 = r.topk().list("top_3_songs")
-print(res33)
-# >>> ['Average lover', 'How will anyone know?', 'Starfish Trooper']
-
-res34 = r.topk().query(
-    "top_3_songs", "Starfish Trooper", "Road to everywhere"
-)
-print(res34)  # >>> [1, 0]
-```
-
-<!-- < clients-example home_prob_dts topk Python >}}
-< /clients-example >}} -->
+{{< clients-example home_prob_dts topk Python >}}
+{{< /clients-example >}}