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4 min readTo split a string using multiple characters in pandas, you can use the str.split() method with a regular expression pattern as the separator. For example, if you want to split a string based on both commas and spaces, you can pass a regex pattern such as '[,\s]+' to the str.split() method. This will split the string whenever it encounters either a comma or a space.
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5 min readTo create heatmaps for different rows in pandas, you can use the seaborn library in conjunction with pandas. First, you need to import both libraries. Then, you can select the rows you want to visualize from your pandas DataFrame and pass them to the seaborn heatmap function. Make sure to set the appropriate parameters such as the colormap and the size of the heatmap. Finally, display the heatmap to visualize the data distribution across the selected rows.
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4 min readTo add a column based on a boolean list in pandas, you can use the loc function to insert values based on the condition provided by the boolean list. By selecting the rows where the boolean list evaluates to True, you can assign a value to a new column in the DataFrame. This allows you to dynamically create a new column based on the conditions specified by the boolean list.[rating:c36a0b44-a88a-44f5-99fb-b0a6f274c6bc]How to combine multiple boolean lists in pandas.
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4 min readTo extract the number before specific strings in pandas, you can use regular expressions in combination with the str.extract function. First, you need to define a regular expression pattern that matches the number before the specific string you are looking for. Then, you can use the str.extract function to extract the matched number from the target column in your pandas DataFrame. This approach allows you to extract the desired number before the specific string efficiently and accurately.
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4 min readTo transform a JSON file into multiple dataframes with pandas, you can use the pd.read_json() function to load the JSON file into a pandas dataframe. Once the data is loaded, you can then manipulate and extract different parts of the data into separate dataframes by using pandas functionality such as selecting columns or filtering rows based on certain conditions.You can also use the json_normalize() function from the pandas library to flatten nested JSON objects into a pandas dataframe.
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4 min readTo search words with numbers and special characters in Solr, you can use the "KeywordTokenizerFactory" tokenizer in your schema.xml file to tokenize the input text without splitting words based on spaces or punctuation. This will allow Solr to index and search for alphanumeric characters along with special characters as a single token.
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3 min readTo combine two pandas series, you can use the append() method or the concat() function.To combine two pandas series using the append() method, you can simply call the append() method on one of the series and pass the other series as an argument. This will append the values of the second series to the first series.Another way to combine two pandas series is to use the concat() function. You can pass a list of series that you want to combine as an argument to the concat() function.
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3 min readTo group by on a list of strings in pandas, you can use the groupby() function along with the agg() function to specify how you want to aggregate the grouped data. First, you need to convert the strings into a pandas DataFrame. Then, you can use the groupby() function to group the data by a specific column or set of columns. Finally, you can use the agg() function to specify how you want to aggregate the data within each group.
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8 min readIn Solr, parallel indexing on files can be done using the DIH (DataImportHandler) feature. First, you would need to define the data import configuration in the solrconfig.xml file, specifying the location of the files to be indexed. Then, you can use the DIH API to trigger parallel indexing on those files.To achieve parallel indexing, you can divide the files into multiple chunks and create multiple threads to process each chunk simultaneously.
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4 min readTo change column names of a pandas series object, you can use the .rename() method. This method allows you to specify new column names by passing a dictionary where the keys are the current column names and the values are the new column names. After specifying the new column names, you can assign the result back to the original series object to apply the changes.
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5 min readWhen dealing with null values in an aggregated table with pandas, you can use the fillna() method to fill those null values with a specified value. This method allows you to replace NaN values with a specific value across the entire DataFrame or on a column-by-column basis. You can also use the ffill() or bfill() methods to fill null values with the previous or next non-null value, respectively.