figure - original and normalized values for price string manipulations raw data presents all sorts of issues and complexities before it can be used for analysis strings are another class of raw data which needs special attention and treatment before our algorithms can make sense out of them as mentioned while discussing wrangling methods for categorical datathere are limitations and issues while directly using string data in algorithms string data representing natural language is highly noisy and requires its own set of steps for wrangling though most of these steps are use case dependentit is worth mentioning them here (we will cover these in detail along with use cases for better claritystring data usually undergoes wrangling steps such astokenizationsplitting of string data into constituent units for examplesplitting sentences into words or words into characters stemming and lemmatizationthese are normalization methods to bring words into their root or canonical forms while stemming is heuristic process to achieve the root formlemmatization utilizes rules of grammar and vocabulary to derive the root stopword removaltext contains words that occur at high frequency yet do not convey much information (punctuationsconjunctionsand so onthese words/phrases are usually removed to reduce dimensionality and noise from data apart from the three common steps mentioned previouslythere are other manipulations like pos tagginghashingindexingand so on each of these are required and tuned based on the data and problem statement on hand stay tuned for more details on these in the coming data summarization data summarization refers to the process of preparing compact representation of raw data at hand this process involves aggregation of data using different statisticalmathematicaland other methods summarization is helpful for visualizationcompressing raw dataand better understanding of its attributes the pandas library provides various powerful summarization techniques to suit different requirements we will cover couple of them here as well the most widely used form of summarization is to group values based on certain conditions or attributes the following snippet illustrates one such summarization print(df['price'][df['user_type']==' 'mean()print(df['purchase_week'value_counts()