DOC-5501 Python prob examples

Author: andy-stark-redis

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

@@ -99,16 +99,45 @@ 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.
 
-{{< clients-example home_prob_dts bloom Python >}}
-{{< /clients-example >}}
+```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 >}} -->
 
 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.
 
-{{< clients-example home_prob_dts cuckoo Python >}}
-{{< /clients-example >}}
+```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 >}} -->
 
 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,
@@ -128,8 +157,27 @@ 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.
 
-{{< clients-example home_prob_dts hyperloglog Python >}}
-{{< /clients-example >}}
+```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 >}} -->
 
 The main benefit that HyperLogLogs offer is their very low
 memory usage. They can count up to 2^64 items with less than
@@ -169,8 +217,35 @@ 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.
 
-{{< clients-example home_prob_dts cms Python >}}
-{{< /clients-example >}}
+```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 >}} -->
 
 The advantage of using a CMS over keeping an exact count with a
 [sorted set]({{< relref "/develop/data-types/sorted-sets" >}})
@@ -202,8 +277,52 @@ 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.
 
-{{< clients-example home_prob_dts tdigest Python >}}
-{{< /clients-example >}}
+```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 >}} -->
 
 A t-digest object also supports several other related commands, such
 as querying by rank. See the
@@ -225,5 +344,47 @@ 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.
 
-{{< clients-example home_prob_dts topk Python >}}
-{{< /clients-example >}}
+```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 >}} -->