4/24/2023 0 Comments Klib mieta newsletter![]() The real purpose of a newsletter is to build relationships. And if you’ve some email copywriting chops, you can drive significant traffic to your podcast. Have a podcast yourself? High-five because that can be one of your newsletter themes. There are several newsletter ideas here, really! 28. You can share a podcast to listen to, a podcast episode that’s Thor-level worthy to tune into, or even discuss an episode. If you’re an avid podcast listener, this is a useful idea. It’s a great way to create FOMO (Shh! Don’t tell anyone, but I’ve joined several communities this way - cause FOMO, you know! □) 27. Do you host a Twitter chat? Discuss what you’ll be chattin’ about. Have a Facebook group? Share what people are talking about. Leah Ryder who runs the Write | Werk newsletter takes a different approach (or, rather an empowering approach) by interviewing females who are crushing it in the content marketing space. You don’t need to limit interviews to talking to experts only. They refer to interviews with marketing leaders as “fireside chats” for readers’ inboxes. Headstart Copywriting does the same in their email newsletter. Planning to create original newsletter content to turn your subscribers into loyal readers? Well, this is it. Let’s get on with it: 125 audience-engaging newsletter ideas to try today 1. So whether you’re only starting a newsletter or someone who’s looking to spice up their email marketing with engaging newsletter content, you’ll find this idea bank pretty helpful. We’ve packed it with fresh email newsletter ideas that you can take for a spin anytime you like. And, nope, it’s no genie doing that for you, but this blog post. Wishing you had more newsletter ideas in your swipe file?Ĭonsider your wish granted. ColumnSelector () # selects num or cat columns, ideal for a Feature Union or Pipeline - klib. cat_pipe () # provides common operations for preprocessing of categorical data - klib. num_pipe () # provides common operations for preprocessing of numerical data - klib. feature_selection_pipe () # provides common operations for feature selection - klib. train_dev_test_split ( df ) # splits a dataset and a label into train, optionally dev and test sets - klib. loss of information # klib.preprocess - functions for data preprocessing (feature selection, scaling. pool_duplicate_subsets ( df ) # pools subset of cols based on duplicates with min. mv_col_handling ( df ) # drops features with high ratio of missing vals based on informational content - klib. drop_missing ( df ) # drops missing values, also called in data_cleaning() - klib. convert_datatypes ( df ) # converts existing to more efficient dtypes, also called inside data_cleaning() - klib. clean_column_names ( df ) # cleans and standardizes column names, also called inside data_cleaning() - klib. data_cleaning ( df ) # performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes.) - klib. missingval_plot ( df ) # returns a figure containing information about missing values # klib.clean - functions for cleaning datasets - klib. dist_plot ( df ) # returns a distribution plot for every numeric feature - klib. ![]() corr_plot ( df ) # returns a color-encoded heatmap, ideal for correlations - klib. corr_mat ( df ) # returns a color-encoded correlation matrix - klib. cat_plot ( df ) # returns a visualization of the number and frequency of categorical features - klib. ![]() ![]() DataFrame ( data ) # scribe - functions for visualizing datasets - klib.
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