chore(code): Do not alias ipython Markdown import
This commit is contained in:
parent
740350eacd
commit
562b1eb6a0
3 changed files with 13 additions and 13 deletions
|
@ -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
|
||||
|
|
10
article.qmd
10
article.qmd
|
@ -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
|
||||
|
|
|
@ -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.
|
||||
|
|
Loading…
Reference in a new issue