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#datadev

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Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.<br>Regression Redress restrains bias by segregating the residual values.<br>My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">data.yt/kit/regression-redress</span><span class="invisible">.html</span></a></p><p><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>accuracy</span></a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RegressionRedress</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scikitLearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span></p><p><a href="https://hachyderm.io/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> on two requirements:<br>* zooming, panning and rescaling<br>* shareable dashboards</p><p>"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: <a href="https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pauliacomi.com/2020/06/07/plot</span><span class="invisible">ly-v-bokeh.html</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/retrieval" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>retrieval</span></a> <a href="https://hachyderm.io/tags/dataMining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataMining</span></a> <a href="https://hachyderm.io/tags/plotly" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>plotly</span></a> <a href="https://hachyderm.io/tags/Dash" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Dash</span></a> <a href="https://hachyderm.io/tags/Bokeh" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bokeh</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/dataInteraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataInteraction</span></a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://hachyderm.io/tags/dataDon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDon</span></a> <a href="https://hachyderm.io/tags/widgets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>widgets</span></a> <a href="https://hachyderm.io/tags/ipython" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ipython</span></a> <a href="https://hachyderm.io/tags/jupyter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>jupyter</span></a> <a href="https://hachyderm.io/tags/dashboards" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dashboards</span></a> <a href="https://hachyderm.io/tags/businessIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>businessIntelligence</span></a></p>
Eric Maugendre<p><a href="https://hachyderm.io/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> Decision-Making Guide</p><p>"How do you decide between <a href="https://hachyderm.io/tags/Plotly" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Plotly</span></a> and <a href="https://hachyderm.io/tags/Seaborn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Seaborn</span></a>?<br>* If you need interactive and dynamic visualizations, especially for dashboards or 3D data, Plotly is the way to go.<br>* If you’re focused on statistical analysis, creating publication-ready visuals, or conducting exploratory data analysis, Seaborn is likely your best choice."<br>by Amit Yadav: <a href="https://medium.com/@amit25173/plotly-vs-seaborn-f7207dd3e642" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">medium.com/@amit25173/plotly-v</span><span class="invisible">s-seaborn-f7207dd3e642</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/retrieval" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>retrieval</span></a> <a href="https://hachyderm.io/tags/dataMining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataMining</span></a></p>
Eric Maugendre<p>´Technical people are blind to the fact they automatically solve dozens of problems every day in their regular workflow, any single one big enough to block another user for a few hours. Without even thinking about it.´</p><p>´There are usually two kinds of coders giving advises. A fresh one that has no idea how complex things really are, yet. Or an experienced one, that forgot it.´</p><p><span class="h-card" translate="no"><a href="https://framapiaf.org/@bitecode" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>bitecode</span></a></span> <a href="https://www.bitecode.dev/p/why-not-tell-people-to-simply-use" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">bitecode.dev/p/why-not-tell-pe</span><span class="invisible">ople-to-simply-use</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/dev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dev</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/install" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>install</span></a> <a href="https://hachyderm.io/tags/anaconda" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>anaconda</span></a> <a href="https://hachyderm.io/tags/packages" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>packages</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tech</span></a> <a href="https://hachyderm.io/tags/packaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>packaging</span></a> <a href="https://hachyderm.io/tags/complexity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>complexity</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span></p><p><a href="https://hachyderm.io/tags/Lasso" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Lasso</span></a> <a href="https://hachyderm.io/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LinearRegression</span></a> "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"</p><p><a href="https://scikit-learn.org/stable/modules/linear_model.html#lasso" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">scikit-learn.org/stable/module</span><span class="invisible">s/linear_model.html#lasso</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sklearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> "practitioners can leverage <a href="https://hachyderm.io/tags/LASSO" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LASSO</span></a> regression to construct more interpretable and predictive models that excel in scenarios involving high-dimensional data and intricate feature relationships."</p><p><a href="https://datasciencedecoded.com/posts/12_LASSO_Regression_Feature_Selection_Predictive_Models" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">datasciencedecoded.com/posts/1</span><span class="invisible">2_LASSO_Regression_Feature_Selection_Predictive_Models</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>How to identify and handle duplicate values: <a href="https://stackabuse.com/handling-duplicate-values-in-a-pandas-dataframe/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">stackabuse.com/handling-duplic</span><span class="invisible">ate-values-in-a-pandas-dataframe/</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataScience</span></a></p>
Eric Maugendre<p>A categorical variable takes on a limited number of values.<br>The categorical <a href="https://hachyderm.io/tags/dataType" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataType</span></a> is useful in the following cases:<br>- A string variable consisting of only some values. df[["label"]].astype("category") saves memory.<br>- The lexical order is not the same as the logical order (“one”, “two”, “three”). Sorting and min/max will use the logical order.<br>- As a signal to other libraries to treat as a category.</p><p>More: <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pandas.pydata.org/pandas-docs/</span><span class="invisible">stable/user_guide/categorical.html</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a></p>
Eric Maugendre<p>"Extract Year from a datetime column", by Piyush Raj: <a href="https://datascienceparichay.com/article/pandas-extract-year-from-datetime-column/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">datascienceparichay.com/articl</span><span class="invisible">e/pandas-extract-year-from-datetime-column/</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/timeSeries" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>timeSeries</span></a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a></p>
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