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DOC-5225 enabled testable Python prob examples #1831

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191 changes: 12 additions & 179 deletions content/develop/clients/redis-py/prob.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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
Expand Down Expand Up @@ -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" >}})
Expand Down Expand Up @@ -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
Expand All @@ -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 >}}