Recap the Fundamentals of Data Analysis with Pandas: A Simple Code-Driven Approach

Let's review the basic of Pandas with these basic functions.

As a data scientist, it can be easy to get lost in the overwhelming number of advanced Python libraries available. However, mastering the basics is essential for building a strong foundation in data science. In this blog post, we will explore the powerful yet elegant Pandas library and provide code examples for some of its fundamental functions. Whether you are a beginner or an experienced practitioner, these examples will help you reinforce your understanding of Pandas and improve your data analysis skills.

df_head:
        name  age  gender   income  married
0      Sara   25  female  50000.0    False
1  Mohammed   30    male  60000.0     True

df_tail:
      name  age  gender   income  married
2     Ali   35    male      NaN     True
3  Khaled   40    male  80000.0    False
4   Asmaa   45  female  90000.0     True

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 5 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   name     5 non-null      object 
 1   age      5 non-null      int64  
 2   gender   5 non-null      object 
 3   income   4 non-null      float64
 4   married  5 non-null      bool   
dtypes: bool(1), float64(1), int64(1), object(2)
memory usage: 293.0+ bytes
df_info:
 None

df_desc:
              age        income
count   5.000000      4.000000
mean   35.000000  70000.000000
std     7.905694  18257.418584
min    25.000000  50000.000000
25%    30.000000  57500.000000
50%    35.000000  70000.000000
75%    40.000000  82500.000000
max    45.000000  90000.00000

df_sort:
        name  age  gender   income  married
0      Sara   25  female  50000.0    False
1  Mohammed   30    male  60000.0     True
2       Ali   35    male      NaN     True
3    Khaled   40    male  80000.0    False
4     Asmaa   45  female  90000.0     True

df_group:
 gender
female    70000.0
male      70000.0
Name: income, dtype: float64

df_pivot:
 married    False    True 
gender                   
female   50000.0  90000.0
male     80000.0  60000.0

df_apply:
    age    income
0   50  100000.0
1   60  120000.0
2   70       NaN
3   80  160000.0
4   90  180000.0

df_fillna:
        name  age  gender   income  married
0      Sara   25  female  50000.0    False
1  Mohammed   30    male  60000.0     True
2       Ali   35    male      0.0     True
3    Khaled   40    male  80000.0    False
4     Asmaa   45  female  90000.0     True

df_isnull:
     name    age  gender  income  married
0  False  False   False   False    False
1  False  False   False   False    False
2  False  False   False    True    False
3  False  False   False   False    False
4  False  False   False   False    False

df_drop_duplicates:
        name  age  gender   income  married
0      Sara   25  female  50000.0    False
1  Mohammed   30    male  60000.0     True
2       Ali   35    male      NaN     True
3    Khaled   40    male  80000.0    False
4     Asmaa   45  female  90000.0     True

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While there are many advanced functions available in Pandas, it's important to master the basics in order to build a strong foundation in data science.