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6 changes: 3 additions & 3 deletions bkp/blogs/ibis-framework/index.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -84,10 +84,10 @@
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import sqlite3\n",
"import ibis"
"\n",
"import ibis\n",
"import matplotlib.pyplot as plt"
]
},
{
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6 changes: 3 additions & 3 deletions pages/blog/console-based-representation-in-astx/index.ipynb
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Expand Up @@ -74,9 +74,9 @@
"outputs": [],
"source": [
"# import display for AST visualization\n",
"from IPython.display import display\n",
"from astx.viz import traverse_ast_ascii, graph_to_ascii\n",
"import astx"
"import astx\n",
"\n",
"from astx.viz import graph_to_ascii, traverse_ast_ascii"
]
},
{
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4 changes: 2 additions & 2 deletions pages/blog/console-based-representation-in-astx/index.md
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Expand Up @@ -54,9 +54,9 @@ The first thing to do is, in your Jupyter Notebook instance, import `display`, w

```python
# import display for AST visualization
from IPython.display import display
from astx.viz import traverse_ast_ascii, graph_to_ascii
import astx

from astx.viz import graph_to_ascii, traverse_ast_ascii
```

Then we create an instance of the Module class, and this instance will be the first node of the tree, or the root node. After that, we declare the variables and literal that will be part of the basic operation that we will parse into an AST.
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Expand Up @@ -183,8 +183,8 @@
"source": [
"#Lazy Evalution with dask\n",
"import dask.dataframe as dd\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Creating a dummy dataset\n",
"num_rows = 100 # Number of rows\n",
Expand Down Expand Up @@ -286,6 +286,7 @@
"# Dynamic task scheduling with Dask\n",
"import dask\n",
"\n",
"\n",
"@dask.delayed\n",
"def square(x):\n",
" return x * x\n",
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Original file line number Diff line number Diff line change
Expand Up @@ -136,8 +136,8 @@ print(result.compute()) # Outputting the computed result
```python
#Lazy Evalution with dask
import dask.dataframe as dd
import pandas as pd
import numpy as np
import pandas as pd

# Creating a dummy dataset
num_rows = 100 # Number of rows
Expand Down Expand Up @@ -230,6 +230,7 @@ print(y_dask.compute())
# Dynamic task scheduling with Dask
import dask


@dask.delayed
def square(x):
return x * x
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Original file line number Diff line number Diff line change
Expand Up @@ -92,6 +92,7 @@
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"NO_COLOR\"] = \"1\""
]
},
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Expand Up @@ -61,6 +61,7 @@ For this tutorial, we will disable the output color feature provided by typer, t

```python
import os

os.environ["NO_COLOR"] = "1"
```

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Expand Up @@ -72,6 +72,7 @@
"source": [
"from plum import dispatch\n",
"\n",
"\n",
"class Processor:\n",
" @dispatch\n",
" def process(self, data: int):\n",
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Expand Up @@ -41,6 +41,7 @@ To demonstrate the basic usage of `plum-dispatch`, let's start with a simple exa
```python
from plum import dispatch


class Processor:
@dispatch
def process(self, data: int):
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13 changes: 12 additions & 1 deletion pages/projects/list/index.md
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Expand Up @@ -170,14 +170,25 @@ projects:
type: incubated
maintainer_name: Ivan Ogasawara
maintainer_email: [email protected]
url: https://osl-incubator.github.io/sugar/
url: https://sugar-org.github.io/sugar/
communication_channel:
provider: discord
url: https://opensciencelabs.org/discord
description: |
Sugar aims to organize your stack of containers, gathering some useful scripts
and keeping this information centralized in a configuration file. So the command
line would be very simple.

- name: swarm-external-secrets
type: incubated
maintainer_name: Sai Sanjay
maintainer_email: [email protected]
url: https://sugar-org.github.io/swarm-external-secrets/
communication_channel:
provider: discord
url: https://opensciencelabs.org/discord
description: |
A Docker Swarm secrets plugin that integrates with multiple secret management providers including HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, and OpenBao.
---

# Affiliated and Incubated Projects
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