chore(code): Do not alias ipython Markdown import

This commit is contained in:
Marty Oehme 2024-07-15 20:22:23 +02:00
parent 740350eacd
commit 562b1eb6a0
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
3 changed files with 13 additions and 13 deletions

View file

@ -2,7 +2,7 @@ from pathlib import Path
import os
import re
## standard imports
from IPython.core.display import Markdown as md
from IPython.display import display, Markdown, HTML
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

View file

@ -90,7 +90,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
```{python}
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
Source: Author's elaboration
@ -211,7 +211,7 @@ def strength_for(val):
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
outp = md(
outp = Markdown(
tabulate(
validity.add_to_findings(
findings_institutional, df_by_intervention, study_strength_bins
@ -680,7 +680,7 @@ Another reason could be the actual implementation of different policy programmes
```{python}
terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
World of work term cluster
@ -693,7 +693,7 @@ World of work term cluster
terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
```
Policy intervention term cluster
@ -704,7 +704,7 @@ Policy intervention term cluster
```{python}
terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
Inequality term cluster

View file

@ -290,7 +290,7 @@ with the search query requiring a term from the general column and one other col
#| label: tbl-wow-terms
#| tbl-cap: World of work term cluster
terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
The world of work cluster, like the inequality and policy intervention clusters below, is made up of a general signifier (such as "work", "inequality" or "intervention") which has to be labelled in a study to form part of the sample,
@ -308,7 +308,7 @@ For the database query, a single term from the general category is required to b
terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
# different headers to include 'social norms'
headers = ["General", "Institutional", "Structural", "Agency & social norms"]
md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
```
Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality,
@ -318,7 +318,7 @@ as seen in @tbl-inequality-terms.
#| label: tbl-inequality-terms
#| tbl-cap: Inequality term cluster
terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
```
A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean 'AND'),
@ -342,7 +342,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
#| label: inclusion-criteria
inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
```
Source: Author's elaboration
@ -543,7 +543,7 @@ def strength_for(val):
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
@ -775,7 +775,7 @@ from src.model import validity
findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
@ -1012,7 +1012,7 @@ from src.model import validity
findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
md(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
Markdown(tabulate(fd_df[["area of policy", "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
```
Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.