Pandas is an open-source data manipulation and analysis library of Python. It is widely used for data manipulation, data preprocessing, and data analysis. Pandas provides several functionalities to merge, join and concatenate two data frames. In this article, we will look at pandas concat merge two data frames within one data frame with code examples.
Concatenation is the process of joining two or more data frames along a particular axis. Pandas provides a function called “concat” which allows us to concatenate two or more data frames. The syntax for concatenating data frames is as follows:
pandas.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None,
levels=None, names=None, verify_integrity=False, sort=False, copy=True)
Here, “objs” parameter specifies the data frames that we want to concatenate.
Let’s take an example where we have two data frames that we want to concatenate:
import pandas as pd
import numpy as np
# Creating first data frame
df1 = pd.DataFrame({
'A': [1,2,3],
'B': [4,5,6],
'C': [7,8,9]
})
# Creating second data frame
df2 = pd.DataFrame({
'A': [10,11,12],
'B': [13,14,15],
'C': [16,17,18]
})
# Concatenating data frames
df = pd.concat([df1, df2])
In this example, we have created two data frames “df1” and “df2”. We have concatenated these data frames using the “concat” function. The concatenated data frame is stored in “df” variable. We have not provided any value for “axis” parameter in the function, so it is considering the default value of “0”. It means that the function will concatenate the data frames along the rows.
The output of the code is shown below:
A B C
0 1 4 7
1 2 5 8
2 3 6 9
0 10 13 16
1 11 14 17
2 12 15 18
As we can observe from the output, the function has concatenated the data frames along the rows.
Now, let’s look at the merge operation in pandas. Merging is the process of joining two data frames based on a specific key. The key is a column or set of columns that are common between two data frames. The syntax for merging data frames is as follows:
pandas.merge(df1, df2, on=None, how='inner', left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None)
Here, “df1” and “df2” are the data frames that we want to merge. The “on” parameter specifies the key based on which we want to merge the data frames.
Let’s take an example where we have two data frames that we want to merge:
# Creating first data frame
df1 = pd.DataFrame({
'key': ['A', 'B', 'C', 'D'],
'value': [1, 2, 3, 4]
})
# Creating second data frame
df2 = pd.DataFrame({
'key': ['C', 'D', 'E', 'F'],
'value': [5, 6, 7, 8]
})
# Merging data frames
df = pd.merge(df1, df2, on='key')
In this example, we have created two data frames “df1” and “df2” with the keys and values. We then merge these data frames using the “merge” function. The merged data frame is stored in “df” variable. We have provided the “on” parameter as “key”, as we want to merge the data frames based on the “key” column.
The output of the code is shown below:
key value_x value_y
0 C 3 5
1 D 4 6
As we can observe from the output, the function has merged the data frames based on the “key” column.
Now, let’s look at how we can concatenate and merge two data frames within one data frame using pandas in Python.
Pandas Concat Merge Two Data frames within One Data frame
Sometimes, we want to concatenate and merge two data frames within one data frame. In order to achieve this, we can use the following steps:
- Concatenate the two data frames.
- Merge the concatenated data frame with the original data frame based on a specific key.
Let’s take an example where we have an original data frame and we want to concatenate and merge two data frames within this data frame:
# Creating original data frame
df = pd.DataFrame({
'A': [1,2,3,4],
'B': [5,6,7,8]
})
# Creating first data frame to concatenate
df1 = pd.DataFrame({
'A': [10,11,12],
'B': [13,14,15]
})
# Creating second data frame to concatenate
df2 = pd.DataFrame({
'A': [20,21,22],
'B': [23,24,25]
})
# Concatenating data frames
df_concat = pd.concat([df1, df2])
# Merging concatenated data frame with original data frame
result = pd.merge(df, df_concat, on='A', how='left')
In this example, we have created an original data frame “df”. We have also created two data frames “df1” and “df2” that we want to concatenate and merge with the original data frame. We have concatenated these data frames using the “concat” function. The concatenated data frame is stored in “df_concat” variable.
We have also merged the concatenated data frame with the original data frame using the “merge” function. We have provided the “on” parameter as “A”, as we want to merge the data frames based on the “A” column. We have also provided the “how” parameter as “left” which means that the merge will be based on the left data frame.
The output of the code is shown below:
A B_x B_y
0 1 5 NaN
1 2 6 13.0
2 3 7 14.0
3 4 8 15.0
As we can observe from the output, the two data frames have been concatenated and merged with the original data frame. The “NaN” value in the output means that there was no value in the concatenated data frame for the corresponding key in the original data frame.
In conclusion, pandas is a powerful tool for data manipulation and analysis in Python. In this article, we have looked at how we can concatenate and merge two data frames within one data frame using pandas in Python. We have also seen the syntax and examples of the “concat” and “merge” functions of pandas. We hope this article will be helpful for you to perform data manipulation and analysis with pandas.
let's go over the previous topics in more detail.
Pandas:
Pandas is one of the most commonly used third-party libraries in Python for data manipulation and analysis. It provides fast, flexible, and easy-to-use data structures that are optimized for dealing with large and complex datasets. Pandas is built on top of the NumPy library and provides various utilities for data loading, cleaning, preparation, transformation, and analysis. It is also known for its efficient handling of missing data and working with time-series data.
Concatenation:
Concatenation is the process of combining two or more data structures (e.g., arrays, series, data frames) into a single one. Pandas provides a concatenation function called “concat”, which concatenates data frames either along rows or columns. The function takes several parameters, such as the data frames to be concatenated, the axis along which to concatenate, whether to preserve the indexes, etc.
Merging:
Merging is the process of combining two or more data frames based on a common key. It is similar to SQL join operation and allows us to combine data frames with different structures and contents. Pandas provides a merging function called “merge”, which takes two data frames, a join type (inner, left, right, or outer), and a join key to combine data frames. Merges can be performed on single or multiple keys, and the function can handle different types of join operations.
Data Frames:
Data frames are one of the most important data structures in pandas. A data frame is a two-dimensional labeled dataset, similar to a spreadsheet or SQL table, where each column can have a different data type. Data frames can be created by loading data from various sources, such as CSV, Excel, SQL databases, or by creating them from Python data structures. Data frames provide various attributes and methods that allow us to access, manipulate, and analyze data in a flexible and efficient manner.
Overall, pandas is a powerful library that provides fast and efficient tools for data manipulation and analysis. It is highly customizable and can be used in a variety of data-related tasks, such as cleaning, preprocessing, analysis, visualization, and more. Pandas is also widely used in machine learning and data science projects, making it an essential library in the Python ecosystem.
Popular questions
Q1. What is the difference between concatenation and merging in pandas?
A1. Concatenation is the process of combining two or more data frames either along rows or columns. Merging is the process of combining two or more data frames based on a common key.
Q2. What is the purpose of the “concat” function in pandas?
A2. The “concat” function in pandas is used to concatenate two or more data frames either along rows or columns.
Q3. How can we merge two data frames in pandas?
A3. We can merge two data frames in pandas using the “merge” function, which takes two data frames, a join type, and a join key to combine data frames.
Q4. Can we concatenate and merge two data frames within one data frame in pandas?
A4. Yes, we can concatenate and merge two data frames within one data frame in pandas by first concatenating the data frames and then merging the concatenated data frame with the original data frame based on a specific key.
Q5. What are some common applications of pandas in data manipulation and analysis?
A5. Some common applications of pandas in data manipulation and analysis include data cleaning, preprocessing, transformation, visualization, and analysis. Pandas is widely used in various industries, such as finance, healthcare, marketing, and more. It is also widely used in machine learning and data science projects for feature engineering and data wrangling.
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