{"id":129760,"date":"2023-05-09T18:32:29","date_gmt":"2023-05-09T13:02:29","guid":{"rendered":"https:\/\/www.vskills.in\/certification\/tutorial\/?page_id=129760"},"modified":"2024-04-12T14:31:49","modified_gmt":"2024-04-12T09:01:49","slug":"pandas-glossary","status":"publish","type":"page","link":"https:\/\/www.vskills.in\/certification\/tutorial\/pandas-glossary\/","title":{"rendered":"Pandas Glossary"},"content":{"rendered":"\n<p>Important definitions and terminologies used in Pandas<\/p>\n\n\n<div id=\"glossary\">\n<div id=\"letter-links\" style=\"font-size: 30px;letter-spacing:20px;\"><a href=\"#A\">A<\/a><a href=\"#C\">C<\/a><a href=\"#D\">D<\/a><a href=\"#E\">E<\/a><a href=\"#F\">F<\/a><a href=\"#G\">G<\/a><a href=\"#H\">H<\/a><a href=\"#I\">I<\/a><a href=\"#J\">J<\/a><a href=\"#K\">K<\/a><a href=\"#L\">L<\/a><a href=\"#M\">M<\/a><a href=\"#N\">N<\/a><a href=\"#O\">O<\/a><a href=\"#P\">P<\/a><a href=\"#Q\">Q<\/a><a href=\"#R\">R<\/a><a href=\"#S\">S<\/a><a href=\"#T\">T<\/a><a href=\"#U\">U<\/a><a href=\"#V\">V<\/a><a href=\"#W\">W<\/a><a href=\"#Z\">Z<\/a><\/div>\n<h2 id=\"A\">A<\/h2>\n<ul>\n<li>Agg function in Pandas<\/li>\n<li>Aggregation in Pandas<\/li>\n<li>API in Pandas<\/li>\n<li>Append in Pandas<\/li>\n<li>Apply in Pandas<\/li>\n<li>Assign in Pandas<\/li>\n<li>autocorr() in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"C\">C<\/h2>\n<ul>\n<li>Categorical data in Pandas<\/li>\n<li>Chunksize in Pandas<\/li>\n<li>comprod() in Pandas<\/li>\n<li>Concatenation in Pandas<\/li>\n<li>copy(obj) in Pandas<\/li>\n<li>corr() in Pandas<\/li>\n<li>cov() in Pandas<\/li>\n<li>Cross-join in Pandas<\/li>\n<li>Crosstab in Pandas<\/li>\n<li>cummin() in Pandas<\/li>\n<li>cumsum() in Pandas<\/li>\n<li>Cut function in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"D\">D<\/h2>\n<ul>\n<li>Data Wrangling in Pandas<\/li>\n<li>DataFrame in Pandas<\/li>\n<li>Dates and times in Pandas<\/li>\n<li>Datetime in Pandas<\/li>\n<li>describe() in Pandas<\/li>\n<li>df.columns = [&#8216;a&#8217;,&#8217;b&#8217;,&#8217;c&#8217;] in Pandas<\/li>\n<li>df.columns in Pandas<\/li>\n<li>df.count() in Pandas<\/li>\n<li>df.dropna() in Pandas<\/li>\n<li>df.dropna(axis=1,thresh=n) in Pandas<\/li>\n<li>df.dropna(axis=1) in Pandas<\/li>\n<li>df.duplicated([subset, keep]) in Pandas<\/li>\n<li>df.fillna(x) in Pandas<\/li>\n<li>df.groupby([col1,col2]) in Pandas<\/li>\n<li>df.groupby(col) in Pandas<\/li>\n<li>df.head(n) in Pandas<\/li>\n<li>df.iloc[row_index, col_index] in Pandas<\/li>\n<li>df.iloc[row_index,:] in Pandas<\/li>\n<li>df.iloc[start_index : end_index, start_index : end_index] in Pandas<\/li>\n<li>df.index in Pandas<\/li>\n<li>df.info() in Pandas<\/li>\n<li>df.loc[label1, label2, &#8230;] in Pandas<\/li>\n<li>df.loc[row_label, col_label] in Pandas<\/li>\n<li>df.loc[start_row_label : end_row_label, start_col_label : end_col_label] in Pandas<\/li>\n<li>df.max() in Pandas<\/li>\n<li>df.mean() in Pandas<\/li>\n<li>df.median() in Pandas<\/li>\n<li>df.min() in Pandas<\/li>\n<li>df.nlargest(n, &#8216;value&#8217;) in Pandas<\/li>\n<li>df.nsmallest(n, &#8216;value&#8217;) in Pandas<\/li>\n<li>df.rename(columns={&#8216;old_name&#8217;: &#8216;new_ name&#8217;}) in Pandas<\/li>\n<li>df.rename(columns=lambda x: x + 1) in Pandas<\/li>\n<li>df.rename(index=lambda x: x + 1) in Pandas<\/li>\n<li>df.set_index(&#8216;column_one&#8217;) in Pandas<\/li>\n<li>df.shape in Pandas<\/li>\n<li>df.sort_values([col1,col2],ascending=[True,False]) in Pandas<\/li>\n<li>df.sort_values(col1) in Pandas<\/li>\n<li>df.sort_values(col2,ascending=False) in Pandas<\/li>\n<li>df.std() in Pandas<\/li>\n<li>df.tail(n) in Pandas<\/li>\n<li>df.to_clipboard() in Pandas<\/li>\n<li>df.to_csv(filename) in Pandas<\/li>\n<li>df.to_excel(filename) in Pandas<\/li>\n<li>df.to_html(filename) in Pandas<\/li>\n<li>df.to_json(filename) in Pandas<\/li>\n<li>df.to_sql(table_name, connection_object) in Pandas<\/li>\n<li>df[[col1, col2]] in Pandas<\/li>\n<li>df[col] in Pandas<\/li>\n<li>df1.append(df2) in Pandas<\/li>\n<li>Diff function in Pandas<\/li>\n<li>diff() in Pandas<\/li>\n<li>drop_duplicates([subset, keep, inplace]) in Pandas<\/li>\n<li>Dropping rows\/columns in Pandas<\/li>\n<li>Dtypes in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"E\">E<\/h2>\n<ul>\n<li>Excel in Pandas<\/li>\n<li>Exploratory data analysis (EDA) in Pandas<\/li>\n<li>Exporting data in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"F\">F<\/h2>\n<ul>\n<li>Filling missing values in Pandas<\/li>\n<li>Filter function in Pandas<\/li>\n<li>Filtering in Pandas<\/li>\n<li>Flatten in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"G\">G<\/h2>\n<ul>\n<li>Geospatial data in Pandas<\/li>\n<li>Grouper in Pandas<\/li>\n<li>Grouping in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"H\">H<\/h2>\n<ul>\n<li>Hierarchical indexing in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"I\">I<\/h2>\n<ul>\n<li>Importing data in Pandas<\/li>\n<li>Imputation in Pandas<\/li>\n<li>Indexing in Pandas<\/li>\n<li>Interpolation in Pandas<\/li>\n<li>Iteration in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"J\">J<\/h2>\n<ul>\n<li>Joining in Pandas<\/li>\n<li>JSON in Pandas<\/li>\n<li>Json_normalize in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"K\">K<\/h2>\n<ul>\n<li>K-fold cross-validation in Pandas<\/li>\n<li>kurt() in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"L\">L<\/h2>\n<ul>\n<li>len(obj) in Pandas<\/li>\n<li>Loc in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"M\">M<\/h2>\n<ul>\n<li>mad() in Pandas<\/li>\n<li>Mapping in Pandas<\/li>\n<li>Masking in Pandas<\/li>\n<li>Memory optimization in Pandas<\/li>\n<li>Merging in Pandas<\/li>\n<li>Multi-index in Pandas<\/li>\n<li>Multi-level pivoting in Pandas<\/li>\n<li>Multi-threading in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"N\">N<\/h2>\n<ul>\n<li>Named aggregation in Pandas<\/li>\n<li>NaN in Pandas<\/li>\n<li>Null values in Pandas<\/li>\n<li>Numpy in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"O\">O<\/h2>\n<ul>\n<li>obj.empty in Pandas<\/li>\n<li>obj.get(key) in Pandas<\/li>\n<li>obj.size in Pandas<\/li>\n<li>obj.truncate([before, after, axis) in Pandas<\/li>\n<li>obj.where(cond, other = NaN, inplace = False, axis = None) in Pandas<\/li>\n<li>One-hot encoding in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"P\">P<\/h2>\n<ul>\n<li>Padding in Pandas<\/li>\n<li>Pandasql in Pandas<\/li>\n<li>Parsing dates in Pandas<\/li>\n<li>pd.DataFrame(dict) in Pandas<\/li>\n<li>pd.isnull() in Pandas<\/li>\n<li>pd.notnull() in Pandas<\/li>\n<li>pd.read_clipboard() in Pandas<\/li>\n<li>pd.read_csv(filename) in Pandas<\/li>\n<li>pd.read_excel(filename) in Pandas<\/li>\n<li>pd.read_html(url) in Pandas<\/li>\n<li>pd.read_json(json_string) in Pandas<\/li>\n<li>pd.read_sql(query, connection_object) in Pandas<\/li>\n<li>pd.read_table(filename) in Pandas<\/li>\n<li>Pivot function in Pandas<\/li>\n<li>Pivot table in Pandas<\/li>\n<li>Plotting in Pandas<\/li>\n<li>Profiling in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"Q\">Q<\/h2>\n<ul>\n<li>Quantile in Pandas<\/li>\n<li>quantile(x) in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"R\">R<\/h2>\n<ul>\n<li>Random sampling in Pandas<\/li>\n<li>Read_csv in Pandas<\/li>\n<li>Read_excel in Pandas<\/li>\n<li>Read_sql in Pandas<\/li>\n<li>Regex in Pandas<\/li>\n<li>Reindex in Pandas<\/li>\n<li>Rename in Pandas<\/li>\n<li>Replace function in Pandas<\/li>\n<li>Replace in Pandas<\/li>\n<li>Resampling in Pandas<\/li>\n<li>Rolling function in Pandas<\/li>\n<li>Rolling window in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"S\">S<\/h2>\n<ul>\n<li>s.fillna(s.mean()) in Pandas<\/li>\n<li>s.iloc[index] in Pandas<\/li>\n<li>s.loc[index] in Pandas<\/li>\n<li>s.replace([1,3],[&#8216;one&#8217;,&#8217;three&#8217;]) in Pandas<\/li>\n<li>s.replace(1,&#8217;one&#8217;) in Pandas<\/li>\n<li>Sample function in Pandas<\/li>\n<li>Saving data in Pandas<\/li>\n<li>Scaling in Pandas<\/li>\n<li>Scikit-learn in Pandas<\/li>\n<li>Seaborn in Pandas<\/li>\n<li>sem() in Pandas<\/li>\n<li>Series in Pandas<\/li>\n<li>Set_index in Pandas<\/li>\n<li>Shift function in Pandas<\/li>\n<li>skew() in Pandas<\/li>\n<li>Sorting in Pandas<\/li>\n<li>Sparse data in Pandas<\/li>\n<li>Split-apply-combine in Pandas<\/li>\n<li>SQL in Pandas<\/li>\n<li>Stack\/unstack in Pandas<\/li>\n<li>String manipulation in Pandas<\/li>\n<li>String methods in Pandas<\/li>\n<li>Styling in Pandas<\/li>\n<li>Subsetting in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"T\">T<\/h2>\n<ul>\n<li>T-test in Pandas<\/li>\n<li>The `df.rename(columns={&#8216;old_name&#8217;: &#8216;new_ name&#8217;}) in Pandas<\/li>\n<li>Tidy data in Pandas<\/li>\n<li>Time series in Pandas<\/li>\n<li>Time zone handling in Pandas<\/li>\n<li>Timestamp in Pandas<\/li>\n<li>Transpose in Pandas<\/li>\n<li>Tuples in Pandas<\/li>\n<li>Type conversion in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"U\">U<\/h2>\n<ul>\n<li>Union in Pandas<\/li>\n<li>Unique in Pandas<\/li>\n<li>Unpivot in Pandas<\/li>\n<li>Upsampling in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"V\">V<\/h2>\n<ul>\n<li>Validation in Pandas<\/li>\n<li>value_counts() in Pandas<\/li>\n<li>var() in Pandas<\/li>\n<li>Vectorization in Pandas<\/li>\n<li>Vectorized operations in Pandas<\/li>\n<li>Visualization in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"W\">W<\/h2>\n<ul>\n<li>Wide vs long format in Pandas<\/li>\n<li>Wide-to-long format conversion. in Pandas<\/li>\n<li>Working with dates in Pandas<\/li>\n<li>Writing data to file in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<h2 id=\"Z\">Z<\/h2>\n<ul>\n<li>Zip file in Pandas<\/li>\n<\/ul>\n<div><a href=\"#top\">Go to Top<\/a><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Important definitions and terminologies used in Pandas ACDEFGHIJKLMNOPQRSTUVWZ A Agg function in Pandas Aggregation in Pandas API in Pandas Append in Pandas Apply in Pandas Assign in Pandas autocorr() in Pandas Go to Top C Categorical data in Pandas Chunksize in Pandas comprod() in Pandas Concatenation in Pandas copy(obj) in Pandas corr() in Pandas cov()&#8230;<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-129760","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas Glossary - Tutorial<\/title>\n<meta name=\"description\" content=\"What is Pandas? 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