Additionally, you will see how these each have different methods, so why you would use one data structure vs.For each of the upcoming data structures you see, it is useful to understand how you index, are they mutable, and are they ordered.We can use the order to access parts of a list and string. Order is about whether the order of the elements in an object matters, and whether this position of an element can be used to access the element.However, strings cannot be changed without creating a completely new object, so they are immutable. Mutability is about whether or not we can change an object once it has been created.There are two things to keep in mind for each of the data types you are using: 3.2.27.25 temporarily define a new column as a function of existing columns.
Precision of list as element of pandas table series#
3.2.27.24 change a Series to the ‘category’ data type (reduces memory usage and increases performance).3.2.27.23 Collapse hierarchical column indexes.3.2.27.22 Split delimited values in a DataFrame column into two new columns.3.2.27.21 Change all NaNs to None (useful before loading to a db).3.2.27.20 Get rid of non-numeric values throughout a DataFrame.3.2.27.19 Loop through rows in a DataFrame.3.2.27.18 concatenate two DataFrames (axis=0 for rows, axis=1 for columns).3.2.27.17 create dummy variables for ‘column_x’ and exclude first dummy column.3.2.27.16 change the data type of a column when reading in a file.3.2.27.15 change the data type of a column.3.2.27.13 setting and then removing an index, resetting index can help remove hierarchical indexes while preserving the table in its basic structure.3.2.27.12 boolean filtering with datetime_column format.3.2.27.11 datetime_column format exposes convenient attributes.3.2.27.10 convert a string to the datetime_column format.3.2.27.8 string methods are accessed via ‘str’.rows become columns, columns become rows) 3.2.27.6 alter values in one column based on values in another column.3.2.27.5 replace all instances of a value in a column (must match entire value).3.2.27.4 count the number of unique values.3.2.27.2 encode strings as integer values (automatically starts at 0).3.2.27.1 map existing values to a different set of values.3.2.26 Merging and Concatenating Dataframes.3.2.22 Lower-case all DataFrame column names.3.2.21 Renaming, Adding, and Removing Columns.3.2.20 Selecting Multiple Columns and Filtering Rows.3.2.11 head, tail, describe, max, memory_usage.3.2.10 Dealing with NaN values (missing data).3.1.3 check whether an index label exists in Series.2.20 Extract elements along the diagonal.1.4.6 get() looks up values in a dictionary.
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