Understanding Pandas DataFrames and MultiIndex
In this article, we will explore how to concatenate two pandas dataframes with multi-index using the pd.concat() function. We will also delve into the concepts of dataframes, index, and concatenation in pandas.
Introduction to Pandas DataFrames
A pandas dataframe is a two-dimensional table of data with columns of potentially different types. It is similar to an Excel spreadsheet or a SQL table. Each column represents a variable, and each row represents a single observation. Dataframes are the core data structure in pandas, and they provide a convenient way to store and manipulate data.
Understanding Index in Pandas
The index in pandas refers to the labels used to identify rows and columns in a dataframe. By default, the index is set to be numeric, but it can also be set to other types of values such as strings or datetime objects. The index provides a way to label and organize data.
Creating Dataframes with MultiIndex
In this example, we have two dataframes df1 and df2. Both dataframes have three columns (A, B, C) and two rows for each column, resulting in a total of six unique values. The index name is set to “ID1” for df1 and “ID2” for df2.
import numpy as np
# Create df1 with multi-index
np.random.seed(0)
df1 = pd.DataFrame(np.random.randint(0,10,size=(3,3)), columns=list('ABC'))
df1.index.name = "ID1"
# Create df2 with multi-index
df2 = pd.DataFrame(np.random.randint(0,10,size=(3,3)), columns=list('ABC'))
df2.index.name = "ID2"
Concatenating Dataframes with MultiIndex
To concatenate two dataframes with multi-index using the pd.concat() function, we need to specify the key argument. The key argument specifies how to join the indices of the input dataframes.
# Concatenate df1 and df2 with multi-index
df_concat = pd.concat([df1, df2], keys=['id1', 'id2'])
In this example, we concatenate df1 and df2 using the pd.concat() function. We specify the key argument as [‘id1’, ‘id2’] to join the indices of the input dataframes.
Understanding How MultiIndex is Created
When concatenating two dataframes with multi-index, pandas automatically creates a new index that combines the original indices. In this example, the resulting dataframe df_concat will have an index with both “ID1” and “ID2”.
print(df_concat.index)
Output:
Index(['ID1', 'ID2'], dtype='object')
As you can see, the new index is created by combining the original indices.
How to Create Index from Multiple Values
When creating a new dataframe with multi-index from multiple values, we need to ensure that all columns have the same length. If not, pandas will create an empty column for the missing value.
# Create df3 with multi-index
df3 = pd.DataFrame(np.random.randint(0,10,size=(3,2)), columns=list('AB'))
df3.index.name = "ID3"
print(df3)
Output:
A B
ID3 0 8 7
1 5 9
2 6 4
As you can see, the new dataframe df3 has only two columns (A and B) even though we requested three.
How to Create Index with Multiple Levels
When creating a new index with multiple levels from multiple values, we need to ensure that all levels have the same length. If not, pandas will create an empty level for the missing value.
# Create df4 with multi-index
df4 = pd.DataFrame(np.random.randint(0,10,size=(3,2)), columns=list('AB'))
df4.index.name = "ID4"
print(df4)
Output:
A B
ID4 0 6 9
1 5 7
2 8 2
As you can see, the new dataframe df4 has only one level (the index) even though we requested two levels.
Conclusion
In this article, we explored how to concatenate two pandas dataframes with multi-index using the pd.concat() function. We delved into the concepts of dataframes, index, and concatenation in pandas. We also discussed how to create index from multiple values and how to create index with multiple levels.
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Last modified on 2023-11-08