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Addition of skew, add_prefix, add_suffix, count, std functions #16

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merged 12 commits into from
Jan 19, 2024
Merged

Addition of skew, add_prefix, add_suffix, count, std functions #16

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6eaee34
Add count and add prefix and sufix implementation
marcosvm13 Dec 18, 2023
f9f396f
Update version Pandas API Notebook
marcosvm13 Dec 18, 2023
531fc85
Added skew and std implementations
nipsn Dec 18, 2023
27f685f
fix typo
marcosvm13 Dec 18, 2023
91caa66
Extends test cases for skew
Dec 19, 2023
348917b
fix problems in test pandas API
marcosvm13 Dec 19, 2023
6127e3b
Applied comment for std function
nipsn Dec 20, 2023
a1ac6d8
Changed value function call to q.value()
nipsn Dec 20, 2023
8e58d37
Add error validation to test
marcosvm13 Dec 20, 2023
689100c
Update typos in documentation
marcosvm13 Dec 20, 2023
004f785
Refactor q code. pflake8 fixes.
rianoc-kx Jan 16, 2024
cbff0f2
Merge pull request #31 from rianoc-kx/feature/prefix_suffix_std_skew_…
neutropolis Jan 17, 2024
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310 changes: 310 additions & 0 deletions docs/user-guide/advanced/Pandas_API.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -646,6 +646,110 @@
"tab.mode(dropna=False)"
]
},
{
"cell_type": "markdown",
"id": "f5c66579",
"metadata": {},
"source": [
"### Table.std()\n",
"\n",
"```\n",
"Table.std(axis=0, skipna=True, numeric_only=False, ddof=0)\n",
"```\n",
"\n",
"Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument.\n",
"\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to calculate the sum across 0 is columns, 1 is rows. | 0 |\n",
"| skipna | bool | not yet implemented | True |\n",
"| numeric_only | bool | Only use columns of the table that are of a numeric data type. | False |\n",
"| ddof | int | Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. | 1 |\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | The std across each row / column with the key corresponding to the row number or column name. |"
]
},
{
"cell_type": "markdown",
"id": "c2767afd",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"Calculate the std across the columns of a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87b94fd0",
"metadata": {},
"outputs": [],
"source": [
"tab = kx.Table(data=\n",
" {\n",
" 'a': [1, 2, 2, 4],\n",
" 'b': [1, 2, 6, 7],\n",
" 'c': [7, 8, 9, 10],\n",
" 'd': [7, 11, 14, 14]\n",
" }\n",
")\n",
"tab"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e54d557",
"metadata": {},
"outputs": [],
"source": [
"tab.std()"
]
},
{
"cell_type": "markdown",
"id": "14950833",
"metadata": {},
"source": [
"Calculate the std across the rows of a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f19161ed",
"metadata": {},
"outputs": [],
"source": [
"tab.std(axis=2)"
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]
},
{
"cell_type": "markdown",
"id": "a8ea5a38",
"metadata": {},
"source": [
"Calculate std accross columns with ddof=0:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6361dcb7",
"metadata": {},
"outputs": [],
"source": [
"tab.std(ddof=0)"
]
},
{
"cell_type": "markdown",
"id": "7e2813b4",
Expand Down Expand Up @@ -1813,6 +1917,136 @@
"df.astype({'c4':kx.SymbolVector, 'c5':kx.SymbolVector})"
]
},
{
"cell_type": "markdown",
"id": "0f8813a0",
"metadata": {},
"source": [
"### Table.add_prefix()\n",
"\n",
"```\n",
"Table.add_prefix(columns)\n",
"```\n",
"\n",
"Rename columns adding a prefix in a table and return the resulting Table object.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :-----: | :-------------: | :------------------------------------------------------------------ | :--------: |\n",
"| prefix | str | The string that will be concatenated with the name of the columns | _required_ |\n",
"| axis | int | Axis to add prefix on. | 0 |\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :---: | :----------------------------------------------------------------- |\n",
"| Table | A table with the given column(s) renamed adding a prefix. |"
]
},
{
"cell_type": "markdown",
"id": "9186ed86",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"The initial table to which a prefix will be added to its columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f20131b",
"metadata": {},
"outputs": [],
"source": [
"tab.head()"
]
},
{
"cell_type": "markdown",
"id": "73c2b08f",
"metadata": {},
"source": [
"Add \"col_\" to table columns:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "926c8295",
"metadata": {},
"outputs": [],
"source": [
"tab.add_prefix(prefix=\"col_\").head()"
]
},
{
"cell_type": "markdown",
"id": "0a4abc8c",
"metadata": {},
"source": [
"### Table.add_sufix()\n",
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"\n",
"```\n",
"Table.add_sufix(columns)\n",
"```\n",
"\n",
"Rename columns adding a sufix in a table and return the resulting Table object.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :-----: | :-------------: | :------------------------------------------------------------------ | :--------: |\n",
"| sufix | str | The string that will be concatenated with the name of the columns | _required_ |\n",
"| axis | int | Axis to add sufix on. | 0 |\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :---: | :----------------------------------------------------------------- |\n",
"| Table | A table with the given column(s) renamed adding a sufix. |"
]
},
{
"cell_type": "markdown",
"id": "c22262b8",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"The initial table to which a sufix will be added to its columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55c1f504",
"metadata": {},
"outputs": [],
"source": [
"tab.head()"
]
},
{
"cell_type": "markdown",
"id": "b4687851",
"metadata": {},
"source": [
"Add \"_col\" to table columns:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e00d0f5c",
"metadata": {},
"outputs": [],
"source": [
"tab.add_sufix(sufix=\"_col\").head()"
]
},
{
"cell_type": "markdown",
"id": "718584f8",
Expand Down Expand Up @@ -2507,6 +2741,82 @@
"tab.prod(numeric_only=True)"
]
},
{
"cell_type": "markdown",
"id": "c87d4f95",
"metadata": {},
"source": [
"### Table.count()\n",
"\n",
"```\n",
"Table.count(axis=0, numeric_only=False)\n",
"```\n",
"\n",
"Returns the count of non-NA values across the given axis.\n",
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"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to count elements across 1 is columns, 0 is rows. | 0 |\n",
"| numeric_only | bool | Only use columns of the table that are of a numeric data type. | False |\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | A dictionary where the key represent the column name / row number and the values are the result of calling `count` on that column / row. |"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6520c195",
"metadata": {},
"outputs": [],
"source": [
"tab.count()"
]
},
{
"cell_type": "markdown",
"id": "ce85797d",
"metadata": {},
"source": [
"### Table.skew()\n",
"\n",
"```\n",
"Table.skew(axis=0, skipna=True, numeric_only=False)\n",
"```\n",
"\n",
"Returns the skewness of all values across the given axis.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to calculate the skewness across 0 is columns, 1 is rows. | 0 |\n",
"| skipna | bool | Ignore any null values along the axis. | True |\n",
"| numeric_only | bool | Only use columns of the table that are of a numeric data type. | False |\n",
"\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | A dictionary where the key represent the column name / row number and the values are the result of calling `skew` on that column / row. |"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fb5dce1",
"metadata": {},
"outputs": [],
"source": [
"tab.skew(numeric_only=True)"
]
},
{
"cell_type": "markdown",
"id": "499025cb",
Expand Down
22 changes: 22 additions & 0 deletions src/pykx/pandas_api/pandas_indexing.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -453,6 +453,28 @@ def rename(self, labels=None, index=None, columns=None, axis=0,
t = _rename_columns(t, columns)

return t

def add_suffix(self, suffix, axis=0):
t = self
if axis == 1:
c_str = 'cols value' if "Keyed" in str(type(t)) else 'cols'
t = q(f'{{(c!`$string[c:{c_str} y],\:string x)xcol y}}', suffix, t)
elif axis == 0:
raise ValueError('nyi')
else:
raise ValueError(f'No axis named {axis}')
return t

def add_prefix(self, prefix, axis=0):
t = self
if axis == 1:
c_str = 'cols value' if "Keyed" in str(type(t)) else 'cols'
t = q(f'{{(c!`$string[x],/:string c:{c_str} y)xcol y}}', prefix, t)
elif axis == 0:
raise ValueError('nyi')
else:
raise ValueError(f'No axis named {axis}')
return t

def sample(self, n=None, frac=None, replace=False, weights=None,
random_state=None, axis=None, ignore_index=False):
Expand Down
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