Answers for "merge in python"

pd merge on multiple columns

new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1','c2'], right_on = ['B_c1','c2'])

Merge Sort python

def merge_sort(arr):
    # The last array split
    if len(arr) <= 1:
        return arr
    mid = len(arr) // 2
    # Perform merge_sort recursively on both halves
    left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])

    # Merge each side together
    return merge(left, right, arr.copy())


def merge(left, right, merged):

    left_cursor, right_cursor = 0, 0
    while left_cursor < len(left) and right_cursor < len(right):
      
        # Sort each one and place into the result
        if left[left_cursor] <= right[right_cursor]:
            merged[left_cursor+right_cursor]=left[left_cursor]
            left_cursor += 1
        else:
            merged[left_cursor + right_cursor] = right[right_cursor]
            right_cursor += 1
            
    for left_cursor in range(left_cursor, len(left)):
        merged[left_cursor + right_cursor] = left[left_cursor]
        
    for right_cursor in range(right_cursor, len(right)):
        merged[left_cursor + right_cursor] = right[right_cursor]

    return merged

join on column pandas

# df1 as main df and use the feild from df2 and map it into df1

df1.merge(df2,on='columnName',how='left')

pandas merge python

import pandas as pd
df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [5, 6, 7, 8]})
df1.merge(df2, left_on='lkey', right_on='rkey')

merge dataframe pandas

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

merge in python

def merge(array, left_index, right_index, middle):
    # Make copies of both arrays we're trying to merge

    # The second parameter is non-inclusive, so we have to increase by 1
    left_copy = array[left_index:middle + 1]
    right_copy = array[middle+1:right_index+1]

    # Initial values for variables that we use to keep
    # track of where we are in each array
    left_copy_index = 0
    right_copy_index = 0
    sorted_index = left_index

    # Go through both copies until we run out of elements in one
    while left_copy_index < len(left_copy) and right_copy_index < len(right_copy):

        # If our left_copy has the smaller element, put it in the sorted
        # part and then move forward in left_copy (by increasing the pointer)
        if left_copy[left_copy_index] <= right_copy[right_copy_index]:
            array[sorted_index] = left_copy[left_copy_index]
            left_copy_index = left_copy_index + 1
        # Opposite from above
        else:
            array[sorted_index] = right_copy[right_copy_index]
            right_copy_index = right_copy_index + 1

        # Regardless of where we got our element from
        # move forward in the sorted part
        sorted_index = sorted_index + 1

    # We ran out of elements either in left_copy or right_copy
    # so we will go through the remaining elements and add them
    while left_copy_index < len(left_copy):
        array[sorted_index] = left_copy[left_copy_index]
        left_copy_index = left_copy_index + 1
        sorted_index = sorted_index + 1

    while right_copy_index < len(right_copy):
        array[sorted_index] = right_copy[right_copy_index]
        right_copy_index = right_copy_index + 1
        sorted_index = sorted_index + 1