Inner Merge

Basic

newDf = pd.merge(df1, df2)
# creates new dataframe with matching columns (based on name) merged into one
# none matching columns have their own column

# or
newDf = df1.merge(df2)


# merge 3
newDf = df1.merge(df2)\
	.merge(df3)

# or

newDf = df1.merge(df2).merge(df3)

Merge Specific Columns

Its common for different IDs to have the same name, i.e. 'ID'

We dont always want this

pd.merge(
    df1,
    df2.rename(columns={'id': 'customer_id'}))

pd.merge(
	leftDf,
	rightDf,
	left_on='colName1',
	right_on='colName2')
# matches the two columns, new column names will be 'colName', the common chars
# we can give it suffixes

pd.merge(
	leftDf,
	rightDf,
	left_on='colName1',
	right_on='colName2',
	suffixes=['_suffForLeft', '_suffForRight',])
# just use this one, trust me 
# look at link if u care that much

https://www.codecademy.com/courses/data-processing-pandas/lessons/pandas-multiple-tables/exercises/left-on-ii

Other merge types

pd.merge(df1, df2, how='outer')
# sets merge to outer
# void values are set to NaN and Null

pd.merge(df1, df2, how='left')
# sets merge to left (df2 may have nulls)

pd.merge(df1, df2, how='right')
# sets merge to right (df1 may have nulls)

Concatenate

sticks 2 df with matching columns together

newdf = pd.concat([df1, df2])