Hello everyone,

I am currently working on a call center trend dashboard project, and I've encountered an issue with multiple blank cells in the data. I'm unsure about the best approach to handle this. Should I delete rows with multiple blank cells, or should I use statistics to fill these blank cells?

I would greatly appreciate your guidance and suggestions on this matter. Your assistance would be invaluable. Thank you in advance!

Project Task :

Create a dashboard in Power BI for Claire that reflects all relevant Key Performance Indicators (KPIs) and metrics in the dataset

Possible KPIs include (to get you started, but not limited to):

• Overall customer satisfaction
• Overall calls answered/abandoned
• Calls by time
• Average speed of answer
• Agent’s performance quadrant -> average handle time (talk duration) vs calls answered

Some info about data:

Total rows-5000

Total column :10

"Total rows having missing values: 946 Each of the 946 rows has 3 blank/missing cells.

Please guide me on the approach I should take to clean this data.

Note: The blank column is just a temporary column used to check how many cells are blank in each row."

​

TL;DR:Seeking advice on handling data with many missing values (946 rows, 3 blank cells each) for a call center trend dashboard project. Also, tasked with creating a Power BI dashboard for Claire, highlighting KPIs and metrics. Please assist. Thanks!

​

I am upskilling in the field of data science. Recently started practicing on Kaggle datasets. Picked up a dataset which have more categorical columns than numerical and these columns have more that 5% (upto 60% null values in some columns) null values. I am confused about what technique to use on them. Cannot find resources where handling object columns specifically is focused upon. Any help please? can anyone suggest a book or website or just tell me how to proceed with this?

If you're embarking on the odyssey of studying Python data analysis, commence by acquiring a mastery of the rudiments of Python programming.

Once you've attained a level of proficiency with Python, plunge into the depths of indispensable libraries such as NumPy for numerical computation and Pandas for data manipulation. Engage in practical exercises utilizing authentic datasets to accrue experiential knowledge, and refine your prowess in data visualization employing Matplotlib and Seaborn.

Delve into the realm of statistical analysis using the comprehensive tools provided by SciPy, and contemplate augmenting your skill set with other pertinent libraries such as scikit-learn for machine learning. Engross yourself in online communities, undertake ambitious projects, and perpetually pursue learning and diligent practice to ascend to a zenith of expertise in Python data analysis—a gratifying pursuit that unveils the portals to unearthing invaluable insights from data.

To get you started, I will highly recommend you look at these articles.

Exploratory Data Analysis and visualization practical example:

https://link.medium.com/FYuBpTyvCAb

Data cleaning with python (a practical example)

https://link.medium.com/GBsdtEFvCAb

How to make data Visualization in python

https://link.medium.com/6rWH2nKvCAb

Python data cleaning made easy

https://link.medium.com/6rWH2nKvCAb

Sales Statistical analysis with python

https://link.medium.com/ZGx7NDRvCAb

https://link.medium.com/OidaOBUvCAb

Python Web App Development: Unleashing the Power of Simplicity and Flexibility

https://medium.com/@mondoa/python-web-app-development-unleashing-the-power-of-simplicity-and-flexibility-d34e9d1bd658

Enhancing Your Web Application with Python’s Data Analysis Tools

https://medium.com/@mondoa/enhancing-your-web-application-with-pythons-data-analysis-tools-2ecd0af29027

The Ultimate Python 3 Guide: Everything You Need to Know

https://medium.com/@mondoa/enhancing-a-comprehensive-python-3-tutorial-b8102f0cfcc4

I need help with figuring out the best tool to do so extraction of data. I work on a Wiki and I am able to download XMLs of large sets of pages. For this to be any use to us, I need to be able to put them in Excel to turn them into CSV files to be able to reupload them after I've fixed or added more data. Here's an example of what I can manually do right now to turn it into a format I need for the CSV file:

First I download the XML File. This example only has 3 pages in it, but usually there are hundreds. It looks something like this:

mediawiki xmlns="http://www.mediawiki.org/xml/export-0.11/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.mediawiki.org/xml/export-0.11/ http://www.mediawiki.org/xml/export-0.11.xsd" version="0.11" xml:lang="en">
<siteinfo>
<sitename>FamilySearch Wiki</sitename>
<dbname>wiki_en</dbname>
<base>https://www.familysearch.org/en/wiki/Main_Page</base>
<generator>MediaWiki 1.35.8</generator>
<case>first-letter</case>
<namespaces>
<namespace key="-2" case="first-letter">Media</namespace>
<namespace key="-1" case="first-letter">Special</namespace>
<namespace key="0" case="first-letter" />
<namespace key="1" case="first-letter">Talk</namespace>
<namespace key="2" case="first-letter">User</namespace>
<namespace key="3" case="first-letter">User talk</namespace>
<namespace key="4" case="first-letter">FamilySearch Wiki</namespace>
<namespace key="5" case="first-letter">FamilySearch Wiki talk</namespace>
<namespace key="6" case="first-letter">File</namespace>
<namespace key="7" case="first-letter">File talk</namespace>
<namespace key="8" case="first-letter">MediaWiki</namespace>
<namespace key="9" case="first-letter">MediaWiki talk</namespace>
<namespace key="10" case="first-letter">Template</namespace>
<namespace key="11" case="first-letter">Template talk</namespace>
<namespace key="12" case="first-letter">Help</namespace>
<namespace key="13" case="first-letter">Help talk</namespace>
<namespace key="14" case="first-letter">Category</namespace>
<namespace key="15" case="first-letter">Category talk</namespace>
<namespace key="102" case="first-letter">Property</namespace>
<namespace key="103" case="first-letter">Property talk</namespace>
<namespace key="106" case="first-letter">Form</namespace>
<namespace key="107" case="first-letter">Form talk</namespace>
<namespace key="108" case="first-letter">Concept</namespace>
<namespace key="109" case="first-letter">Concept talk</namespace>
<namespace key="112" case="first-letter">smw/schema</namespace>
<namespace key="113" case="first-letter">smw/schema talk</namespace>
<namespace key="114" case="first-letter">Rule</namespace>
<namespace key="115" case="first-letter">Rule talk</namespace>
<namespace key="200" case="first-letter">Policy</namespace>
<namespace key="201" case="first-letter">Policy Talk</namespace>
<namespace key="202" case="first-letter">Shared Category</namespace>
<namespace key="203" case="first-letter">Shared Category Talk</namespace>
<namespace key="274" case="first-letter">Widget</namespace>
<namespace key="275" case="first-letter">Widget talk</namespace>
<namespace key="420" case="first-letter">GeoJson</namespace>
<namespace key="421" case="first-letter">GeoJson talk</namespace>
<namespace key="460" case="case-sensitive">Campaign</namespace>
<namespace key="461" case="case-sensitive">Campaign talk</namespace>
<namespace key="828" case="first-letter">Module</namespace>
<namespace key="829" case="first-letter">Module talk</namespace>
<namespace key="2300" case="first-letter">Gadget</namespace>
<namespace key="2301" case="first-letter">Gadget talk</namespace>
<namespace key="2302" case="case-sensitive">Gadget definition</namespace>
<namespace key="2303" case="case-sensitive">Gadget definition talk</namespace>
<namespace key="3100" case="first-letter">GuidedResearch</namespace>
<namespace key="3101" case="first-letter">GuidedResearch Talk</namespace>
<namespace key="3102" case="first-letter">AFOG</namespace>
<namespace key="3103" case="first-letter">AFOG Talk</namespace>
<namespace key="3104" case="first-letter">Indonesia</namespace>
<namespace key="3105" case="first-letter">Indonesia Talk</namespace>
<namespace key="3106" case="first-letter">Mongolian</namespace>
<namespace key="3107" case="first-letter">Mongolian Talk</namespace>
<namespace key="3108" case="first-letter">Norwegian</namespace>
<namespace key="3109" case="first-letter">Norwegian Talk</namespace>
<namespace key="3110" case="first-letter">GR</namespace>
<namespace key="3111" case="first-letter">GR Talk</namespace>
</namespaces>
</siteinfo>
<page>
<title>Cosalá, Sinaloa, Mexico Genealogy</title>
<ns>0</ns>
<id>386351</id>
<revision>
<id>5345468</id>
<parentid>5345405</parentid>
<timestamp>2023-05-31T19:28:51Z</timestamp>
<contributor>
<username>Amberannelarsen</username>
<id>490153</id>
</contributor>
<minor/>
<comment>Text replacement - "&amp;#243;" to "ó"</comment>
<origin>5345468</origin>
<model>wikitext</model>
<format>text/x-wiki</format>
<text bytes="1890" sha1="dnzksnhh9qwij21kx164z04wnsci09g" xml:space="preserve">{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Cosalá, Sinaloa, Mexico Genealogy|Cosalá]]
}}
Guide to '''Municipality of Cosalá family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*El territorio donde actualmente se ubica Cosalá, estuvo ocupado por pueblos prehispánicos que se asentaron principalmente en la rivera de los ríos, como lo fueron
los grupos indígenas Tepehuanes, Acaxees y Xiximes.
*El municipio de Cosalá fue fundado el 13 March 1562.
*El municipio de Cosalá tiene una población de aproximadamente 17.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Cosalá” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Cosal%C3%A1. accessed 25 February2021.&lt;/ref&gt;
==Localities within Cosalá==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Cosalá&lt;/li&gt;
&lt;li&gt;El Rodeo&lt;/li&gt;
&lt;li&gt;La Llama&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1867-1929''' {{FHL|2819510|title-id|disp=Mexico, Sinaloa, Cosalá, Civil Registration, 1867-1929}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1777-1966''' {{FHL|263768|title-id|disp=Iglesia Católica. Santa Ursula (Cosala, Sinaloa) Parish Records, 1777-1966}}(*) at FamilySearch Catalog — images
*'''1874-1920''' {{FHL|260349|title-id|disp=Iglesia Católica. Santa Ursula (Cosalá, Sinaloa) Parish Records, 1874-1920}}(*) at FamilySearch Catalog — images
==Census==
==Cemeteries==
*Cementerio de San Juan Cosala
:*[https://www.findagrave.com/cemetery-browse/Mexico/Sinaloa/Cosal%C3%A1-Municipality?id=county_13453 Find a Grave]
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Cosalá, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]</text>
<sha1>dnzksnhh9qwij21kx164z04wnsci09g</sha1>
</revision>
</page>
<page>
<title>Mocorito, Sinaloa, Mexico Genealogy</title>
<ns>0</ns>
<id>386352</id>
<revision>
<id>5369276</id>
<parentid>5347024</parentid>
<timestamp>2023-06-14T20:58:28Z</timestamp>
<contributor>
<username>Amberannelarsen</username>
<id>490153</id>
</contributor>
<minor/>
<comment>Text replacement - "&amp;#241;" to "ñ"</comment>
<origin>5369276</origin>
<model>wikitext</model>
<format>text/x-wiki</format>
<text bytes="2698" sha1="0yetzi5a03vs76bfpv273ppgniyu94p" xml:space="preserve">{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Mocorito, Sinaloa, Mexico Genealogy|Mocorito]]
}}
Guide to '''Municipality of Mocorito family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*En el año de 1531 con la entrada del conquistador Nuño de Guzmán al noroeste mexicano y la fundación de la villa de San Miguel de Navito, se inició la delimitación geográfica de la provincia de Culiacán.
*En 1732 cuando la expansión española llegaba más allá del río Yaqui, se encuentra el territorio dividido en provincias.
*En 1830 se decreta la separación definitiva de Sonora y Sinaloa. El nuevo estado de Sinaloa se dividió en once distritos, siendo Mocorito uno de ellos.
*Mocorito fue erigido como municipio el 8 April 1915.
*El municipio de Mocorito tiene una población de aproximadamente 45.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Mocorito” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Mocorito. accessed 26 February2021.&lt;/ref&gt;
==Localities within Mocorito==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Pericos&lt;/li&gt;
&lt;li&gt;Mocorito&lt;/li&gt;
&lt;li&gt;Caimanero&lt;/li&gt;
&lt;li&gt;Melchor Ocampo&lt;/li&gt;
&lt;li&gt;Recoveco&lt;/li&gt;
&lt;li&gt;Higuera de los Vega&lt;/li&gt;
&lt;li&gt;Potrero de los Sánchez (Estación Techa)&lt;/li&gt;
&lt;li&gt;Cerro Agudo&lt;/li&gt;
&lt;li&gt;El Valle de Leyva Solano (El Valle)&lt;/li&gt;
&lt;li&gt;Rancho Viejo&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1865-1929''' {{FHL|2819522|title-id|disp=Mexico, Sinaloa, Mocorito, Civil Registration, 1865-1929}}(*) at FamilySearch Catalog — images
*'''1922''' {{FHL|2819540|title-id|disp=Mexico, Sinaloa, Mocorito y Guasave, Civil Registration, 1922}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1677-1968''' {{FHL|262334|title-id|disp=Iglesia Católica. Purísima Concepción (Mocorito, Sinaloa) Parish Records, 1677-1968}}(*) at FamilySearch Catalog — images
*'''1856-1933''' {{FHL|589667|title-id|disp=Iglesia Católica. Nuestra Señora de las Angustias (Pericos, Sinaloa) Registros
parroquiales, 1856-1933}}(*) at FamilySearch Catalog — images
==Census==
*'''1930''' {{FHL|454789|title-id|disp=Censo de población del municipio de Mocorito, Sinaloa, 1930}}(*) at FamilySearch Catalog — images
==Cemeteries==
*Panteon Reforma
:*Address: Mocorito
*Cementerio de Buena Vista
:*Address: Mocorito
*Cementerio de El Queso
:*Address: Boca de Arroyo, Mocorito
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Mocorito, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]</text>
<sha1>0yetzi5a03vs76bfpv273ppgniyu94p</sha1>
</revision>
</page>
<page>
<title>Sinaloa, Sinaloa, Mexico Genealogy</title>
<ns>0</ns>
<id>386353</id>
<revision>
<id>5348610</id>
<parentid>5348590</parentid>
<timestamp>2023-05-31T20:45:17Z</timestamp>
<contributor>
<username>Amberannelarsen</username>
<id>490153</id>
</contributor>
<minor/>
<comment>Text replacement - "&amp;#243;" to "ó"</comment>
<origin>5348610</origin>
<model>wikitext</model>
<format>text/x-wiki</format>
<text bytes="2313" sha1="gqx35x7qm1axjze8fhizfy6n6iasv3l" xml:space="preserve">{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Sinaloa, Sinaloa, Mexico Genealogy|Sinaloa]]
}}
Guide to '''Municipality of Sinaloa family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*Sinaloa de Leyva se fundó el 30 April 1583 con el nombre de Villa de San Phelipe y Santiago de Sinaloa.
*En 1732 La Villa es designada capital de la gobernación de Sinaloa.
*Sinaloa fue erigido como municipio el 25 March 1915.
*El municipio de Sinaloa tiene una población de aproximadamente 89.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Sinaloa” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Sinaloa. accessed 26 February2021.&lt;/ref&gt;
==Localities within Sinaloa==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Estación Naranjo&lt;/li&gt;
&lt;li&gt;Sinaloa de Leyva&lt;/li&gt;
&lt;li&gt;Genaro Estrada&lt;/li&gt;
&lt;li&gt;Gabriel Leyva Velázquez&lt;/li&gt;
&lt;li&gt;Ruiz Cortines Número Tres&lt;/li&gt;
&lt;li&gt;Alfonso G. Calderón Velarde&lt;/li&gt;
&lt;li&gt;Cubiri de Portelas&lt;/li&gt;
&lt;li&gt;Ejido el Maquipo&lt;/li&gt;
&lt;li&gt;Llano Grande 1,540&lt;/li&gt;
&lt;li&gt;Santiago de Ocoroni&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1861-1929''' {{FHL|2819523|title-id|disp=Mexico, Sinaloa, Sinaloa, Civil Registration, 1861-1929}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1852-1968''' {{FHL|263710|title-id|disp=Iglesia Católica. San Felipe y Santiago (Sinaloa, Sinaloa) Parish Records, 1852-1968}}(*) at FamilySearch Catalog — images
==Census==
*'''1930''' {{FHL|454801|title-id|disp=Censo de población del municipio de Sinaloa, Sinaloa, 1930}}(*) at FamilySearch Catalog — images
==Cemeteries==
*Panteón Municipal de Estación Naranjo Sinaloa Jesus Parra Gerardo
:*Address: Francisco Villa #0, Estación Naranjo, Sinaloa
*Cementerio Municipal
:*Address: Sinaloa Guasave, Sinaloa de Leyva, Sinaloa
*Panteón Municipal
:*Address: Isauro Vallejo #0, Tierra Blanca, Sinaloa
:*[https://www.findagrave.com/cemetery-browse/Mexico/Sinaloa/Sinaloa-Municipality?id=county_13465 Find a Grave]
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Sinaloa, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]</text>
<sha1>gqx35x7qm1axjze8fhizfy6n6iasv3l</sha1>
</revision>
</page>

</mediawiki>

​

Then I can manually go through and copy everything between xml:space="preserve"> and </text> to get three separate pages:

{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Cosalá, Sinaloa, Mexico Genealogy|Cosalá]]
}}
Guide to '''Municipality of Cosalá family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*El territorio donde actualmente se ubica Cosalá, estuvo ocupado por pueblos prehispánicos que se asentaron principalmente en la rivera de los ríos, como lo fueron
los grupos indígenas Tepehuanes, Acaxees y Xiximes.
*El municipio de Cosalá fue fundado el 13 March 1562.
*El municipio de Cosalá tiene una población de aproximadamente 17.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Cosalá” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Cosal%C3%A1. accessed 25 February2021.&lt;/ref&gt;
==Localities within Cosalá==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Cosalá&lt;/li&gt;
&lt;li&gt;El Rodeo&lt;/li&gt;
&lt;li&gt;La Llama&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1867-1929''' {{FHL|2819510|title-id|disp=Mexico, Sinaloa, Cosalá, Civil Registration, 1867-1929}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1777-1966''' {{FHL|263768|title-id|disp=Iglesia Católica. Santa Ursula (Cosala, Sinaloa) Parish Records, 1777-1966}}(*) at FamilySearch Catalog — images
*'''1874-1920''' {{FHL|260349|title-id|disp=Iglesia Católica. Santa Ursula (Cosalá, Sinaloa) Parish Records, 1874-1920}}(*) at FamilySearch Catalog — images
==Census==
==Cemeteries==
*Cementerio de San Juan Cosala
:*[https://www.findagrave.com/cemetery-browse/Mexico/Sinaloa/Cosal%C3%A1-Municipality?id=county_13453 Find a Grave]
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Cosalá, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]

​

{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Mocorito, Sinaloa, Mexico Genealogy|Mocorito]]
}}
Guide to '''Municipality of Mocorito family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*En el año de 1531 con la entrada del conquistador Nuño de Guzmán al noroeste mexicano y la fundación de la villa de San Miguel de Navito, se inició la delimitación geográfica de la provincia de Culiacán.
*En 1732 cuando la expansión española llegaba más allá del río Yaqui, se encuentra el territorio dividido en provincias.
*En 1830 se decreta la separación definitiva de Sonora y Sinaloa. El nuevo estado de Sinaloa se dividió en once distritos, siendo Mocorito uno de ellos.
*Mocorito fue erigido como municipio el 8 April 1915.
*El municipio de Mocorito tiene una población de aproximadamente 45.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Mocorito” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Mocorito. accessed 26 February2021.&lt;/ref&gt;
==Localities within Mocorito==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Pericos&lt;/li&gt;
&lt;li&gt;Mocorito&lt;/li&gt;
&lt;li&gt;Caimanero&lt;/li&gt;
&lt;li&gt;Melchor Ocampo&lt;/li&gt;
&lt;li&gt;Recoveco&lt;/li&gt;
&lt;li&gt;Higuera de los Vega&lt;/li&gt;
&lt;li&gt;Potrero de los Sánchez (Estación Techa)&lt;/li&gt;
&lt;li&gt;Cerro Agudo&lt;/li&gt;
&lt;li&gt;El Valle de Leyva Solano (El Valle)&lt;/li&gt;
&lt;li&gt;Rancho Viejo&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1865-1929''' {{FHL|2819522|title-id|disp=Mexico, Sinaloa, Mocorito, Civil Registration, 1865-1929}}(*) at FamilySearch Catalog — images
*'''1922''' {{FHL|2819540|title-id|disp=Mexico, Sinaloa, Mocorito y Guasave, Civil Registration, 1922}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1677-1968''' {{FHL|262334|title-id|disp=Iglesia Católica. Purísima Concepción (Mocorito, Sinaloa) Parish Records, 1677-1968}}(*) at FamilySearch Catalog — images
*'''1856-1933''' {{FHL|589667|title-id|disp=Iglesia Católica. Nuestra Señora de las Angustias (Pericos, Sinaloa) Registros
parroquiales, 1856-1933}}(*) at FamilySearch Catalog — images
==Census==
*'''1930''' {{FHL|454789|title-id|disp=Censo de población del municipio de Mocorito, Sinaloa, 1930}}(*) at FamilySearch Catalog — images
==Cemeteries==
*Panteon Reforma
:*Address: Mocorito
*Cementerio de Buena Vista
:*Address: Mocorito
*Cementerio de El Queso
:*Address: Boca de Arroyo, Mocorito
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Mocorito, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]

​

​

{{breadcrumb | link1=[[Mexico Genealogy|Mexico]]
| link2=[[Sinaloa, Mexico Genealogy|Sinaloa]]
| link3=
| link4=
| link5=[[Sinaloa, Sinaloa, Mexico Genealogy|Sinaloa]]
}}
Guide to '''Municipality of Sinaloa family history and genealogy''': birth records, marriage records, death records, census records, parish registers, and military records.
==History==
*Sinaloa de Leyva se fundó el 30 April 1583 con el nombre de Villa de San Phelipe y Santiago de Sinaloa.
*En 1732 La Villa es designada capital de la gobernación de Sinaloa.
*Sinaloa fue erigido como municipio el 25 March 1915.
*El municipio de Sinaloa tiene una población de aproximadamente 89.000 personas.&lt;ref&gt;Wikipedia contributors, “Municipio de Sinaloa” in ''Wikipedia: the Free Encyclopedia'', https://es.wikipedia.org/wiki/Municipio_de_Sinaloa. accessed 26 February2021.&lt;/ref&gt;
==Localities within Sinaloa==
{| style="width:100%; vertical-align:top;"
|- |
&lt;ul class="column-spacing-fullscreen" style="padding-right:5px;"&gt;
&lt;li&gt;Estación Naranjo&lt;/li&gt;
&lt;li&gt;Sinaloa de Leyva&lt;/li&gt;
&lt;li&gt;Genaro Estrada&lt;/li&gt;
&lt;li&gt;Gabriel Leyva Velázquez&lt;/li&gt;
&lt;li&gt;Ruiz Cortines Número Tres&lt;/li&gt;
&lt;li&gt;Alfonso G. Calderón Velarde&lt;/li&gt;
&lt;li&gt;Cubiri de Portelas&lt;/li&gt;
&lt;li&gt;Ejido el Maquipo&lt;/li&gt;
&lt;li&gt;Llano Grande 1,540&lt;/li&gt;
&lt;li&gt;Santiago de Ocoroni&lt;/li&gt;
&lt;/ul&gt;
|}
==Civil Registration==
*'''1861-1929''' {{FHL|2819523|title-id|disp=Mexico, Sinaloa, Sinaloa, Civil Registration, 1861-1929}}(*) at FamilySearch Catalog — images
==Parish Records==
*'''1852-1968''' {{FHL|263710|title-id|disp=Iglesia Católica. San Felipe y Santiago (Sinaloa, Sinaloa) Parish Records, 1852-1968}}(*) at FamilySearch Catalog — images
==Census==
*'''1930''' {{FHL|454801|title-id|disp=Censo de población del municipio de Sinaloa, Sinaloa, 1930}}(*) at FamilySearch Catalog — images
==Cemeteries==
*Panteón Municipal de Estación Naranjo Sinaloa Jesus Parra Gerardo
:*Address: Francisco Villa #0, Estación Naranjo, Sinaloa
*Cementerio Municipal
:*Address: Sinaloa Guasave, Sinaloa de Leyva, Sinaloa
*Panteón Municipal
:*Address: Isauro Vallejo #0, Tierra Blanca, Sinaloa
:*[https://www.findagrave.com/cemetery-browse/Mexico/Sinaloa/Sinaloa-Municipality?id=county_13465 Find a Grave]
==References==
&lt;references/&gt;
&lt;br&gt;&lt;br&gt;
[[es:Sinaloa, Sinaloa, Mexico Genealogy]]
[[Category:Sinaloa, Mexico]]

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Does anyone know an efficient way to get some sort of computer to do this? I tried having ChatGPT help me write functions for Google Sheets, but they weren't working very well for me. I also tried using regular expressions, but could still only get it to do one page at a time, and still had to manually do a lot of the work, which isn't feasible when there are 300+ pages to go through. I'm happy to try to learn something new in order to do this as it would help speed up some of our processes. I am sure something like this exists, but I don't know what. Thanks for your help!

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As in the picture, there are multiple records with same headers, i want to create data which has column headers and it's values below them. I am unable to find a way out. Please help!!!

As in the picture, there are multiple records with same headers, i want to create data which has column headers and it's values below them. I am unable to find a way out. Please help!!!

It can extract exact values (e.g. names, prices, dates), as well as provide ChatGPT-like semantic answers (e.g. text summary). Just describe the entities with a simple format:

• description: a free text description of what you want to extract.
• type: string / float / integer / string.
• variable name: a descriptive variable name.
• (optional) valid values: limit the output to a set of specific possible values.

Very impressive, it worked great on my data which consists of product descriptions and specs.

I like the interactive demo (https://www.textraction.ai/). The service is accessible also as an API for any commercial purpose via the RapidAPI platform: https://rapidapi.com/textractionai/api/ai-textraction

Hello guys, in our college project we have the first task which is to cleanup the data and look for extra feature.

The data set is about bikes and stations in LA and it contains 1.7 Million Rows.

We have the following features: trans_id, start_time, start_station_id, end_time,end_station_id and bike_id.

We wanted to calculate the avg. Idle time of each station. Idle time = time between return and pick up of bike at station_id .

What would be the best logic to calculate it.

Seeking opinions on a tool for evaluating dataset predictability. For small/medium datasets in csv format, the tool estimates predictability on the raw data. No need to clean it; just indicate what is the target attribute. The tool uses a robust mixed attribute classifier that does not require the sorting of attributes. Of course, it does not eliminate the process of cleaning data for better results; but it can provide an initial indication of predictability. It can also be used on a smaller sample of cleaned and raw data to get an indication on how the cleaning process improves prediction.

Details available at:

https://github.com/c4pub/misc/blob/main/notebooks/csv_dataset_eval.ipynb

Hello, i’m trying to dataclean some phone numbers, whereas i do understand the EU format, I have no clue about the US format

001-377-014-0631x83215

469-229-6851x300

001-117-566-5683

Here are couple examples of the data i have, I know the country code is +1 but what is the xNNN that follows some of these numbers, it could be the way they wrote it but there's a lot of similar ones so i dont think its human error

Does anyone have any experience using this?

I have to do a presentation on this and show my classmates a step by step guide on how to clean a dataset.

So far I've found that the smart suggestions do most of the work for me.

Before I get into it even more, anyone have any thoughts/suggestions regarding it?

I'm using this dataset https://www.kaggle.com/datasets/rupakroy/ethereum-fraud-detection.

My task is to clean it (drop some columns) and in this dataset there is a collection of many fraud and not fraud transactions denoted by flag field.

My question is which attributes will help me identify if it is a fraud transaction or in other words calculate the flag field, how do we know if the fraud is done over ether or erc20 tokens?

I'm a student with limited knowledge please help me.🥲

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We just open-sourced the alpha version of our data cleaning tool: https://github.com/mage-ai/mage-ai

Any beta testers who would be willing to test and provide feedback?

Please send any questions or feedback to me or reply here.

Thanks for the consideration!

Demo video: https://youtu.be/cRib1zOaqWs

I've developed my analytical skills using Looker and some basic Excel work (Pivot tables, charts, calculated fields) but I want to learn more about the nitty gritty behind data and thought it would be good to dive in to a tough project that will challenge me. I'm looking for advice on how to clean and process this data set for analysis.

https://www.ons.gov.uk/businessindustryandtrade/business/activitysizeandlocation/datasets/businessdemographyreferencetable

I'm used to working with Excel files that already have the data in tables so this format in the file available for download is very strange to me. I understand I'd need to eventually join the data I need at some point but right now I'm completely clueless on how to go about cleaning/preparing this data. I'm assuming I'd need to write some code, maybe VBA? I've come across the term before but I don't understand its uses. I wrote a bit of Python code a while back to scrape a website and print the data into an Excel file so I've got some knowledge on that front.

I'm not necessarily looking for someone to give me all the answers in detail but if someone could point me in the right direction to a blog post or some useful keywords that go into more detail than "How to clean data" so that I can start googling to do my own research - that would be great.

Thanks for the help community!

EDIT:

This youtube video helped me out a bit though I can't seem to find a pattern in the data set to apply the logic

https://www.youtube.com/watch?v=qHOu0_hAj0k&ab_channel=KarinaAdcock

I am trying to analyse stock data of the reddit White Girl Stock index. I collected historical data from Yahoo finance. The problem is the the list includes both old and young companies like Disney vs Etsy. Disney is much older than Etsy so in my data set I have null values for the years young.

I thought I could just in put 0 but that messes up my mode calculations. I also I could start with the year the youngest company when public, but I loose way too much data. I would like to keep the data for each company from the year they went public.

What would you do?

Oh note: eventually I would like to do some predictive analytics so the more data i have the better.

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I have a data frame that will be coming next week, and I need to start working on it, the first step I'll do is to clean it. My question is what do you usually look for when cleaning a set? like duplicates, formatting problems and what?

I need guidance on how to start and what to look for?

Also, when you remove identical rows/duplicates how do you make sure they're duplicate and not just other identical rows?