Created with over a decade of experience.
Pandas merge()
Pandas merge() merges two DataFrames together
Pandas sample()
sample() randomly selects a given number of rows
Pandas astype()
astype() cast pandas object to specified datatype
Pandas isin()
isin() checks if value of df1 is contained in df2
Pandas head()
head() returns the first n rows of a pandas object
Pandas shift()
shifts values of pandas object along desired axis
Pandas describe()
describe() provides statistical summary of dataset
Pandas rank()
rank() compute the rank of each element in the df
Pandas groupby()
groups rows based on columns,performs agg function
Pandas pivot()
pivot() reshapes data
Pandas quantile()
returns values at the given quantile over the axis
Pandas diff()
calculate the difference of two dataframes
Pandas join()
allows to combine two dfs based on their indexes
Pandas reset_index()
resets the index of a DataFrame
Pandas assign()
creates a new column in df or modify existing one
Pandas to_dict()
converts a Dataframe into a dictionary
Pandas apply()
applies a function along the axis of a dataframe
Pandas drop()
removes rows or columns from a dataframe
Pandas iloc[]
used for integer-location base data selection
Pandas fillna()
fills missing values (NaN) in a DataFrame
Pandas melt()
reshapes DataFrame from wide format to long format
Pandas drop_duplicates()
drops duplicate rows from a DataFrame
Pandas sort()
sorts a DataFrame by one or more columns
Pandas DataFrame filter()
filters rows/columns from a df based on condition
Pandas dropna()
drops missing values (NaN) from a dataframe
Pandas ewm()
provides function to emphasize more on recent data
Pandas nunique()
returns the number of unique values over the axis
Pandas explode()
transform elements of list-like element to row
Pandas to_csv()
used to write to a CSV file
Pandas loc[]
used to select data from a DataFrame
Pandas rolling()
used to perform rolling window calculations
Pandas count()
used to count non-missing values
Pandas set_index()
used to set the index of a DataFrame
Pandas mean()
compute the arithmetic mean of a set of numbers
Pandas iterrows()
used to iterate over the rows of a DataFrame
Pandas replace()
used to replace values in a DataFrame
Pandas rename()
used to rename columns or index labels
Pandas cumsum()
used to provide the cumulative sum of elements
Pandas mask()
replace values where certain conditions are met
Pandas prod()
used to calculate the product of the values
Pandas round()
used to round values to a specified number
Pandas items()
used to iterate over the columns
Pandas first()
used to select the first n rows of data
Pandas abs()
used to compute the absolute value of each element
Pandas multiply()
used to multiply elements within a DataFrames
Pandas transpose()
used to interchange rows and columns
Pandas aggregate()
used to perform summary computations on data
Pandas sum()
used to calculate the sum of a DataFrame
Pandas where()
used to replace values in a DataFrame
Pandas div()
used to divide one DataFrame by another DataFrame
Pandas at[]
used to get a single value from a DataFrame
Pandas all()
used to check if all elements in a DataFrame
Pandas itertuples()
used to iterate over the rows of a DataFrame
Pandas get_dummies()
converts categorical variable into dummy variable
Pandas slice()
selects a range of rows or columns by their labels
Pandas plot()
allows to create types of plots and visualization
Pandas reindex()
allows to change the index, columns, or both of df
Pandas read_csv()
convert a CSV file into a DataFrame
Pandas to_sql()
write records stored in a df to a SQL database
Pandas resample()
converts time series data to a different frequency
Pandas pivot_table()
allows us to create spreadsheet-style pivot table
Pandas from_dict()
convert a dictionary into a Pandas DataFrame.
Pandas std()
computes standard deviation of set of numerics
Pandas to_excel()
write a DataFrame to an Excel file.
Pandas query()
extract rows from df that satisfy given condition
Pandas to_json()
convert a DataFrame to a JSON-formatted string
Pandas hist()
plot histograms to summarize the data distribution
Pandas insert()
inserts column into a df at a specified location
Pandas boxplot()
create box plots to show the distribution of data
Pandas corr()
compute pairwise correlation coefficient of column
Pandas update()
update df in place using non-NA value from next df
Pandas notnull()
detect existing (non-missing) values in the data
Pandas copy()
creates a separate copy of a DataFrame
Pandas duplicated()
mark duplicate rows based on column values.
Pandas var()
computes the variance of a dataset.
Pandas info()
provides a concise summary of a DataFrame.
Pandas mode()
returns the mode(s) of a dataset.
Pandas to_string()
convert a DataFrame into a string representation
Pandas crosstab()
allows us to create contingency tables.
Pandas read_excel()
allows us to import data from excel files.