Skip to content

Commit

Permalink
Reordered nb documentation
Browse files Browse the repository at this point in the history
  • Loading branch information
@nipsn
nipsn committed Feb 13, 2024
1 parent f51a061 commit 0019cab
Showing 1 changed file with 48 additions and 48 deletions.
96 changes: 48 additions & 48 deletions docs/user-guide/advanced/Pandas_API.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1884,70 +1884,42 @@
"id": "d98b298c",
"metadata": {},
"source": [
"### Table.idxmin()\n",
"### Table.min()\n",
"\n",
"```\n",
"Table.idxmax(axis=0, skipna=True, numeric_only=False)\n",
"Table.min(axis=0, skipna=True, numeric_only=False)\n",
"```\n",
"\n",
"Return index of first occurrence of minimum over requested axis.\n",
"Returns the minimum value across the given axis.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to calculate the idxmin across 0 is columns, 1 is rows. | 0 |\n",
"| axis | int | The axis to calculate the minimum 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",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | A dictionary where the key represents the column name / row number and the values are the result of calling `idxmin` on that column / row. |"
]
},
{
"cell_type": "markdown",
"id": "143f5483",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"Calculate the idxmin across the columns of a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da7cbf8f",
"metadata": {},
"outputs": [],
"source": [
"tab.idxmin()"
]
},
{
"cell_type": "markdown",
"id": "fb531e00",
"metadata": {},
"source": [
"Calculate the idxmin across the rows of a table using only columns thar are of a numeric data type"
"| Dictionary | A dictionary where the key represents the column name / row number and the values are the result of calling `min` on that column / row. |"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9907226a",
"id": "9f13e8a7",
"metadata": {},
"outputs": [],
"source": [
"tab.idxmin(axis=1, numeric_only=True)"
"tab.min()"
]
},
{
"cell_type": "markdown",
"id": "d98b298c",
"id": "b52627d2",
"metadata": {},
"source": [
"### Table.idxmax()\n",
Expand Down Expand Up @@ -1975,7 +1947,7 @@
},
{
"cell_type": "markdown",
"id": "143f5483",
"id": "838a07dd",
"metadata": {},
"source": [
"**Examples:**\n",
Expand All @@ -1986,7 +1958,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "da7cbf8f",
"id": "6c6d3384",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -1995,7 +1967,7 @@
},
{
"cell_type": "markdown",
"id": "fb531e00",
"id": "30738846",
"metadata": {},
"source": [
"Calculate the idxmax across the rows of a table using only columns thar are of a numeric data type"
Expand All @@ -2004,7 +1976,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "9907226a",
"id": "edf9e128",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -2013,40 +1985,68 @@
},
{
"cell_type": "markdown",
"id": "301ab2c2",
"id": "fdb4114c-640a-41ac-a4e7-6c236e9d93ea",
"metadata": {},
"source": [
"### Table.min()\n",
"### Table.idxmin()\n",
"\n",
"```\n",
"Table.min(axis=0, skipna=True, numeric_only=False)\n",
"Table.idxmax(axis=0, skipna=True, numeric_only=False)\n",
"```\n",
"\n",
"Returns the minimum value across the given axis.\n",
"Return index of first occurrence of minimum over requested axis.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to calculate the minimum across 0 is columns, 1 is rows. | 0 |\n",
"| axis | int | The axis to calculate the idxmin 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",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | A dictionary where the key represents the column name / row number and the values are the result of calling `min` on that column / row. |"
"| Dictionary | A dictionary where the key represents the column name / row number and the values are the result of calling `idxmin` on that column / row. |"
]
},
{
"cell_type": "markdown",
"id": "57053c36-932e-4805-916c-4cd5e3d33e82",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"Calculate the idxmin across the columns of a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f13e8a7",
"id": "da7cbf8f",
"metadata": {},
"outputs": [],
"source": [
"tab.min()"
"tab.idxmin()"
]
},
{
"cell_type": "markdown",
"id": "fb531e00",
"metadata": {},
"source": [
"Calculate the idxmin across the rows of a table using only columns thar are of a numeric data type"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9907226a",
"metadata": {},
"outputs": [],
"source": [
"tab.idxmin(axis=1, numeric_only=True)"
]
},
{
Expand Down

0 comments on commit 0019cab

Please sign in to comment.