Pandas Dataframe Methods

Pandas DataFrames are the cornerstone of data manipulation, offering an extensive suite of methods for effective data analysis. It deals with methods like merge() to merge datasets, groupby() to group data for analysis and pivot() to pivot tables for better insights.

NumPy reshape()

Gives a new shape to an array

NumPy where()

where() returns indices of an array that are True

NumPy transpose()

transpose() swaps the axes of the given array

NumPy concatenate()

concatenate() joins arrays elements together

NumPy vstack()

vstack() stacks the input arrays vertically

NumPy append()

append() appends the values at the end of an array

NumPy sort()

sort() method sorts an array in ascending order

NumPy shape()

shape() method returns the shape of an array

NumPy ravel()

ravel() flattens array without changing its data

NumPy repeat()

repeat() repeats the elements of the array

NumPy hstack()

hstack() stacks the arrays horizontally

NumPy stack()

stack() joins a sequence of arrays along new axis

NumPy argmax()

argmax() returns the index of the largest element

NumPy roll()

roll() shifts array elements by specified times

NumPy tile()

tile() constructs an array by repeating arrays

NumPy argsort()

argsort() returns indices that sorts an array

NumPy squeeze()

squeeze() removes the dimensions with size 1

NumPy argmin()

argmin() returns index of smallest array element

NumPy insert()

insert() adds the values at specified indices

NumPy nonzero()

nonzero() finds indices of elements that aren't 0

NumPy argwhere()

returns indices of non-zero elements as 2-D array

NumPy delete()

delete() deletes the values at specified indices

NumPy flip()

flip() reverses the order of the elements

NumPy dstack()

dstack() stacks the sequence of arrays depthwise

NumPy flatten()

flatten() flattens array without changing its data

NumPy astype()

astype() converts an array to a specified datatype

NumPy tolist()

tolist() converts a NumPy array to a Python list

NumPy pad()

pad() adds a value around array axis

NumPy split()

split() splits an array into many sub-arrays

NumPy apply_along_axis()

applies function to axis of nd arrays,avoids loops

NumPy apply_over_axes()

apply a function repeatedly over multiple axes