Expanding Horizons: Applications of Numpy and Pandas in Bioinformatics, Astronomy, and Social Networks

Introduction

Greetings, aspiring data enthusiasts! As we continue to navigate the expansive universe of Numpy and Pandas, it's time for us to examine their applications across various fields. Numpy and Pandas have made their presence known in the world of data science, and they have broadened their relevance into fields such as Bioinformatics, Astronomy, and Social Networks . This lesson will explore these cross-disciplinary applications and discover how Numpy and Pandas strengthen these fields with robust data manipulation capabilities.

Bioinformatics and Data Manipulation

Bioinformatics combines biology and computer science, providing a platform for analyzing and interpreting complex biological data, particularly genetic data. Bioinformatics is often confronted with vast and intricate datasets that require advanced data manipulation techniques to extract valuable insights. Consider a realistic illustration of bioinformatics data - DNA sequences. These sequences are strings of characters representing nucleotides labeled as A, T, C, G. Here's a pandas DataFrame that encapsulates the DNA sequences of several genes. Pythonimport pandas as pd # DNA sequences for several genes data = { "Gene": ["Gene A", "Gene B", "Gene C", "Gene D"], "Sequence": ["ATCGTACGA", "CGATCGATG", "TAGCTAG", "CGTAGCTA"] } df_genes = pd.DataFrame(data) print(df_genes) """ Gene Sequence 0 Gene A ATCGTACGA 1 Gene B CGATCGATG 2 Gene C TAGCTAG 3 Gene D CGTAGCTA """import pandas as pd # DNA sequences for several genes data = { "Gene": ["Gene A", "Gene B", "Gene C", "Gene D"], "Sequence": ["ATCGTACGA", "CGATCGATG", "TAGCTAG", "CGTAGCTA"] } df_genes = pd.DataFrame(data) print(df_genes) """ Gene Sequence 0 Gene A ATCGTACGA 1 Gene B CGATCGATG 2 Gene C TAGCTAG 3 Gene D CGTAGCTA """ In the DataFrame df_genes, each row corresponds to a distinct gene. For instance, the first row provides information about "Gene A," with the sequence "ATCGTACGA". Now, suppose we wish to determine the length of these sequences. Pandas allow us to fulfill this requirement with relative ease. Let's use the Pandas apply function, a versatile function that applies a function along an axis of the DataFrame. In this case, the function is used to compute the length of each DNA sequence in our DataFrame. We then add this data as a new column, Length, to our DataFrame: Pythondf_genes['Length'] = df_genes['Sequence'].apply(len) print(df_genes) """ Gene Sequence Length 0 Gene A ATCGTACGA 9 1 Gene B CGATCGATG 9 2 Gene C TAGCTAG 7 3 Gene D CGTAGCTA 8 """df_genes['Length'] = df_genes['Sequence'].apply(len) print(df_genes) """ Gene Sequence Length 0 Gene A ATCGTACGA 9 1 Gene B CGATCGATG 9 2 Gene C TAGCTAG 7 3 Gene D CGTAGCTA 8 """ As we can see, we used the apply function to apply the len function to the 'Sequence' column, calculated the length of each sequence, and added the result to a new column, 'Length'. This form of operation is a staple of data manipulation in bioinformatics.

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Python

import pandas as pd

# DNA sequences for several genes
data = {
    "Gene": ["Gene A", "Gene B", "Gene C", "Gene D"],
    "Sequence": ["ATCGTACGA", "CGATCGATG", "TAGCTAG", "CGTAGCTA"]
}

df_genes = pd.DataFrame(data)
print(df_genes)
"""
     Gene   Sequence
0  Gene A  ATCGTACGA
1  Gene B  CGATCGATG
2  Gene C    TAGCTAG
3  Gene D   CGTAGCTA
"""

Python

df_genes['Length'] = df_genes['Sequence'].apply(len)
print(df_genes)
"""
     Gene   Sequence  Length
0  Gene A  ATCGTACGA       9
1  Gene B  CGATCGATG       9
2  Gene C    TAGCTAG       7
3  Gene D   CGTAGCTA       8
"""

Python

import numpy as np

# Star dataset (Simulated data for demonstration)
data = {
    "Star_ID": np.arange(1, 5),
    "Right_Ascension": [204.85, 63.70, 305.29, 45.2],
    "Declination": [-29.72, 38.03, -14.78, 7.8],
    "Magnitude": [2.04, 1.25, 3.17, 1.9],
    "Observation_Date": pd.date_range('01/01/2020', periods=4)
}

df_stars = pd.DataFrame(data)
print(df_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
0        1           204.85       -29.72       2.04       2020-01-01
1        2            63.70        38.03       1.25       2020-01-02
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

import numpy as np

# Star dataset (Simulated data for demonstration)
data = {
    "Star_ID": np.arange(1, 5),
    "Right_Ascension": [204.85, 63.70, 305.29, 45.2],
    "Declination": [-29.72, 38.03, -14.78, 7.8],
    "Magnitude": [2.04, 1.25, 3.17, 1.9],
    "Observation_Date": pd.date_range('01/01/2020', periods=4)
}

df_stars = pd.DataFrame(data)
print(df_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
0        1           204.85       -29.72       2.04       2020-01-01
1        2            63.70        38.03       1.25       2020-01-02
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

Python

filter_date = pd.to_datetime('2020-01-02')
filtered_stars = df_stars[df_stars['Observation_Date'] > filter_date]
print(filtered_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

filter_date = pd.to_datetime('2020-01-02')
filtered_stars = df_stars[df_stars['Observation_Date'] > filter_date]
print(filtered_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

Python

# Social interaction data (Simulated for demonstration)
data = {
    "Person": ["Alice", "Bob", "Charlie", "Dave"],
    "Friends": [10, 5, 8, 2],
    "Posts": [100, 50, 80, 200]
}

df_social = pd.DataFrame(data)
print(df_social)
"""
    Person  Friends  Posts
0    Alice       10    100
1      Bob        5     50
2  Charlie        8     80
3     Dave        2    200
"""

# Social interaction data (Simulated for demonstration)
data = {
    "Person": ["Alice", "Bob", "Charlie", "Dave"],
    "Friends": [10, 5, 8, 2],
    "Posts": [100, 50, 80, 200]
}

df_social = pd.DataFrame(data)
print(df_social)
"""
    Person  Friends  Posts
0    Alice       10    100
1      Bob        5     50
2  Charlie        8     80
3     Dave        2    200
"""

Python

df_social['Posts_per_Friend'] = df_social['Posts'] / df_social['Friends']
print(df_social)
"""
    Person  Friends  Posts  Posts_per_Friend
0    Alice       10    100              10.0
1      Bob        5     50              10.0
2  Charlie        8     80              10.0
3     Dave        2    200             100.0
"""

df_social['Posts_per_Friend'] = df_social['Posts'] / df_social['Friends']
print(df_social)
"""
    Person  Friends  Posts  Posts_per_Friend
0    Alice       10    100              10.0
1      Bob        5     50              10.0
2  Charlie        8     80              10.0
3     Dave        2    200             100.0
"""

Python

import numpy as np

# Star dataset (Simulated data for demonstration)
data = {
    "Star_ID": np.arange(1, 5),
    "Right_Ascension": [204.85, 63.70, 305.29, 45.2],
    "Declination": [-29.72, 38.03, -14.78, 7.8],
    "Magnitude": [2.04, 1.25, 3.17, 1.9],
    "Observation_Date": pd.date_range('01/01/2020', periods=4)
}

df_stars = pd.DataFrame(data)
print(df_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
0        1           204.85       -29.72       2.04       2020-01-01
1        2            63.70        38.03       1.25       2020-01-02
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

Python

filter_date = pd.to_datetime('2020-01-02')
filtered_stars = df_stars[df_stars['Observation_Date'] > filter_date]
print(filtered_stars)
"""
   Star_ID  Right_Ascension  Declination  Magnitude Observation_Date
2        3           305.29       -14.78       3.17       2020-01-03
3        4            45.20         7.80       1.90       2020-01-04
"""

Python

# Social interaction data (Simulated for demonstration)
data = {
    "Person": ["Alice", "Bob", "Charlie", "Dave"],
    "Friends": [10, 5, 8, 2],
    "Posts": [100, 50, 80, 200]
}

df_social = pd.DataFrame(data)
print(df_social)
"""
    Person  Friends  Posts
0    Alice       10    100
1      Bob        5     50
2  Charlie        8     80
3     Dave        2    200
"""

Python

df_social['Posts_per_Friend'] = df_social['Posts'] / df_social['Friends']
print(df_social)
"""
    Person  Friends  Posts  Posts_per_Friend
0    Alice       10    100              10.0
1      Bob        5     50              10.0
2  Charlie        8     80              10.0
3     Dave        2    200             100.0
"""