Data Analysis using Pandas methods

Descriptive statistics: Pandas provides functions to compute common summary statistics like mean, median, mode, standard deviation, etc.
Filtering and slicing: You can filter data based on specific criteria or select subsets of data using various methods.
Aggregation and grouping: Pandas allows grouping data based on one or more variables and performing aggregations on them.
Merging and joining: You can combine multiple datasets based on common columns or indices.
Time series analysis: Pandas has excellent support for working with time series data, including date/time indexing and resampling.

append()	Adding rows list , dictionary to DataFrame
Settings	Managing DataFrame display option values
apply	Applying function along an Axis or elements
at	Get and Set data using rows and columns
astype	Cast to a specified dtype
cut	Using segments for categorizing values
describe	Descriptive statistics of DataFrame or series
groupby	combining data and aggregate functions
get_dummies	Convert categorical variable into dummy/indicator variables
head	First n rows of the DataFrame
isnull	Checking NaN or None data
iloc	Values at different position using integer
info	Detail information about the Dataframe
loc	Values at different position using column label
filter	Condition based filtering of rows
fillna	Filling NaN data
merge	combining DataFrame and aggregate functions
methods	Pandas DataFrame methods
nlargest	n elements in descending sorted values
mask	conditional replacement of data
query	Filtering data by using conditions
count	Number of rows or columns with different options
max	Max value of required axis
mean	Mean value of required axis
min	Min value of required axis
rolling	rolling window calculations
shape, size, ndim	Dimensions of the DataFrame
std	Standard Deviation on required axis
sum	Sum of values of required axis
set_index	Creating index using one or more columns
sort_values	Sort columns in ascending or descending
tolist	From DataFrame to List
notnull	Not None and Not Null values checking
pivot()	Reshaped data based on column values
pivot_table()	Reshaped data with aggregate functions based on column values
reset_index	Remove index of the DataFrame
value_counts	counts of unique values
unique	Unique Data of a column
where	Data updating based on condition

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