Join, Merge, Append and Concatenate

Working with multiple data frames often involves joining two or more tables to in bring out more no. of columns from another table by joining on some sort of relationship which exists within a table or appending two tables which is adding one or more table over another table with keeping the same order of columns.

Example of append data -> monthly files of revenue sheets of a company and wee need at end of the year to be clubbed into single table.

Example of merging -> multiple files regarding employee education, compensation, performance all linked to each other in some identifier in each one of them which maps to employee master table and for doing analysis we need data from each of these tables in the same which can be achieved by merging.

We’ll look out for merging/joining two tables now and later will discuss the possibilities around appending to tables using pandas.
To begin with let’s get create some dummy datasets.

import pandas as pd
states_codes = pd.DataFrame({'State': ['Haryana', 'Punjab', 'Rajasthan', 'Uttar Pradesh', 'Madhya Pradesh'],
                        'Code': ['HR', 'PB', 'RJ', 'UP', 'MP']})
states_area = pd.DataFrame({'State': ['Haryana', 'Punjab',  'Uttar Pradesh', 'Bihar'],
                        'Area_InSquareKM': [44212, 50362, 243290, 94165]})
# a dummy data file
states_literacyrate = pd.read_csv('literacy.csv')

states_codes

	State	Code
0	Haryana	HR
1	Punjab	PB
2	Rajasthan	RJ
3	Uttar Pradesh	UP
4	Madhya Pradesh	MP

```python states_area ```

	State	Area_InSquareKM
0	Haryana	44212
1	Punjab	50362
2	Uttar Pradesh	243290
3	Bihar	94165

```python states_literacyrate ```

	State	Year	Literacy Rate
0	Haryana	2011	76.64
1	Rajasthan	2011	67.06
2	Uttar Pradesh	2011	69.72
3	Haryana	2001	67.91
4	Uttar Pradesh	2001	56.27
5	Rajasthan	2001	60.41

As you can see State column repeats across all three tables, meaning to say that in case it is required to pull out from these three table

# Merge vs Join

area_codes_inner = pd.merge(states_area,states_codes)
area_codes_inner

	State	Area_InSquareKM	Code
0	Haryana	44212	HR
1	Punjab	50362	PB
2	Uttar Pradesh	243290	UP

```python results_inner = pd.merge(area_codes_inner,states_literacyrate) results_inner ```

	State	Area_InSquareKM	Code	Year	Literacy Rate
0	Haryana	44212	HR	2011	76.64
1	Haryana	44212	HR	2001	67.91
2	Uttar Pradesh	243290	UP	2011	69.72
3	Uttar Pradesh	243290	UP	2001	56.27

```python area_codes_left = pd.merge(states_area,states_codes,how ='left') area_codes_left ```

	State	Area_InSquareKM	Code
0	Haryana	44212	HR
1	Punjab	50362	PB
2	Uttar Pradesh	243290	UP
3	Bihar	94165	NaN

```python results_left = pd.merge(area_codes_left,states_literacyrate,how ='left') results_left ```

	State	Area_InSquareKM	Code	Year	Literacy Rate
0	Haryana	44212	HR	2011.0	76.64
1	Haryana	44212	HR	2001.0	67.91
2	Punjab	50362	PB	NaN	NaN
3	Uttar Pradesh	243290	UP	2011.0	69.72
4	Uttar Pradesh	243290	UP	2001.0	56.27
5	Bihar	94165	NaN	NaN	NaN

```python area_codes_right = pd.merge(states_area,states_codes,how ='right') area_codes_right ```

	State	Area_InSquareKM	Code
0	Haryana	44212.0	HR
1	Punjab	50362.0	PB
2	Uttar Pradesh	243290.0	UP
3	Rajasthan	NaN	RJ
4	Madhya Pradesh	NaN	MP

```python results_right = pd.merge(area_codes_right,states_literacyrate,how ='right') results_right ```

	State	Area_InSquareKM	Code	Year	Literacy Rate
0	Haryana	44212.0	HR	2011	76.64
1	Haryana	44212.0	HR	2001	67.91
2	Uttar Pradesh	243290.0	UP	2011	69.72
3	Uttar Pradesh	243290.0	UP	2001	56.27
4	Rajasthan	NaN	RJ	2011	67.06
5	Rajasthan	NaN	RJ	2001	60.41

```python area_codes_outer = pd.merge(states_area,states_codes, how = 'outer') area_codes_outer ```

	State	Area_InSquareKM	Code
0	Haryana	44212.0	HR
1	Punjab	50362.0	PB
2	Uttar Pradesh	243290.0	UP
3	Bihar	94165.0	NaN
4	Rajasthan	NaN	RJ
5	Madhya Pradesh	NaN	MP

```python results_outer = pd.merge(area_codes_outer,states_literacyrate,how ='outer') results_outer ```

	State	Area_InSquareKM	Code	Year	Literacy Rate
0	Haryana	44212.0	HR	2011.0	76.64
1	Haryana	44212.0	HR	2001.0	67.91
2	Punjab	50362.0	PB	NaN	NaN
3	Uttar Pradesh	243290.0	UP	2011.0	69.72
4	Uttar Pradesh	243290.0	UP	2001.0	56.27
5	Bihar	94165.0	NaN	NaN	NaN
6	Rajasthan	NaN	RJ	2011.0	67.06
7	Rajasthan	NaN	RJ	2001.0	60.41
8	Madhya Pradesh	NaN	MP	NaN	NaN

other options in merge function are - left_on right_on on left_index right_index sort

a detailed description is can be found here - https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html

states_area

	State	Area_InSquareKM
0	Haryana	44212
1	Punjab	50362
2	Uttar Pradesh	243290
3	Bihar	94165

```python states_codes ```

	State	Code
0	Haryana	HR
1	Punjab	PB
2	Rajasthan	RJ
3	Uttar Pradesh	UP
4	Madhya Pradesh	MP

-- .merge : For column(s)-on-columns(s) operations -- .join : Join DataFrames using their indexes., if need to be on specific keys, then set keys to be the index

states_area.join(states_literacyrate,lsuffix='_FromArea',rsuffix='_FromLiteracy', how='outer')

	State_FromArea	Area_InSquareKM	State_FromLiteracy	Year	Literacy Rate
0	Haryana	44212.0	Haryana	2011	76.64
1	Punjab	50362.0	Rajasthan	2011	67.06
2	Uttar Pradesh	243290.0	Uttar Pradesh	2011	69.72
3	Bihar	94165.0	Haryana	2001	67.91
4	NaN	NaN	Uttar Pradesh	2001	56.27
5	NaN	NaN	Rajasthan	2001	60.41

```python pd.merge(states_area,states_literacyrate, left_index=True, right_index=True, how='outer') ```

	State_x	Area_InSquareKM	State_y	Year	Literacy Rate
0	Haryana	44212.0	Haryana	2011	76.64
1	Punjab	50362.0	Rajasthan	2011	67.06
2	Uttar Pradesh	243290.0	Uttar Pradesh	2011	69.72
3	Bihar	94165.0	Haryana	2001	67.91
4	NaN	NaN	Uttar Pradesh	2001	56.27
5	NaN	NaN	Rajasthan	2001	60.41

```python ## merge on multiple columns ```

city_states_literacy = pd.read_csv('City_States_Literacy.csv')

city_states_area = pd.read_csv('City_States_Area.csv')

city_states_area

	State	City	Area_InSquareKM
0	Haryana	1	983
1	Haryana	2	886
2	Rajasthan	1	881
3	Rajasthan	2	980
4	Rajasthan	3	895
5	Rajasthan	4	1010
6	Rajasthan	5	1075
7	Bihar	1	802
8	Bihar	2	859
9	Bihar	3	1020
10	Uttar Pradesh	4	945
11	Uttar Pradesh	5	787
12	Madhya Pradhes	6	983

```python results_inner = pd.merge(city_states_literacy,city_states_area, left_on=['State','City'], right_on=['State','City']) results_inner ```

	State	City	Literacy Rate	Area_InSquareKM
0	Haryana	1	74.78	983
1	Haryana	2	63.80	886
2	Rajasthan	1	61.68	881
3	Rajasthan	2	68.61	980
4	Rajasthan	3	70.78	895
5	Rajasthan	4	75.80	1010
6	Rajasthan	5	80.65	1075
7	Uttar Pradesh	4	73.77	945
8	Uttar Pradesh	5	60.61	787

```python pd.merge(city_states_literacy,city_states_area, left_on=['State','City'], right_on=['State','City']) ```

	State	City	Literacy Rate	Area_InSquareKM
0	Haryana	1	74.78	983
1	Haryana	2	63.80	886
2	Rajasthan	1	61.68	881
3	Rajasthan	2	68.61	980
4	Rajasthan	3	70.78	895
5	Rajasthan	4	75.80	1010
6	Rajasthan	5	80.65	1075
7	Uttar Pradesh	4	73.77	945
8	Uttar Pradesh	5	60.61	787

---

Related Posts

Read data into pandas

Subsetting a dataframe in pandas

Connect to azure datalake store using python

Table of Contents