Top 20 Must-Know Pandas Code for Newbies

Welcome to an interesting library in Python! I'm Brahma 👋, a passionate software developer. I am documenting my learning journey through a series of blog posts. Stay tuned!!

Introduction

Pandas, unlike the ones in the cover picture 😂, can be complicated at times to understand and implement. So, I am going to share Top 20 must know codes to call yourself a LinkedIn Pandas expert 😂. Jokes aside let's delve into this.

Creating a Pandas DataFrame

The first step before performing any operation on some data is importing it. Ofc!!😂

1. The if-you-don't-have-data method:

   Sounds funny 😂. Coz it is!!

    data = pd.DataFrame(np.arange(10).reshape(5,2), index=['Row1','Row2','Row3','Row4','Row5'], columns=['Column1','Column2'])
    data.head()
   

   So, let's assume Kaggle is an alien for you then you can use the above method to generate a demo dataset. Ofc won't advise that but it's okay if you don't have a dataset 🫠.

2. The all-customised method:
   Using dictionaries and lists to create a dataframe.

    import pandas as pd
   
    # Creating a DataFrame from a dictionary
    data = {
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'Los Angeles', 'Chicago']
    }
    df = pd.DataFrame(data)
    print(df)
   
    # Creating a DataFrame from a list of lists
    data = [
        ['Alice', 25, 'New York'],
        ['Bob', 30, 'Los Angeles'],
        ['Charlie', 35, 'Chicago']
    ]
    df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])
    print(df)
   

   No wonder why would you do that 🫠.

3. The famous method:

   Importing data from a csv file.

    # Reading the CSV file into a DataFrame
    df = pd.read_csv('data.csv')
    print(df)
    df = pd.read_csv('data.csv', index_col=0) # basically makes the 1st column as the index column
    print(df)
   

4. The jsonified method:

   As the name suggests its importing from a json file.

    import pandas as pd
    # Read JSON file into a DataFrame
    df = pd.read_json('data.json')
    # Display the DataFrame
    print(df)
   

Accessing the Data

1. Thelocmethod:

    df.loc[row_label, column_label]
   

2. Theilocmethod (my fav 😍):

    df.iloc[row_index, column_index]
   

Inspecting the Data

1. head&tail:

    import pandas as pd
   
    # Create a sample DataFrame
    data = {
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
    }
    df = pd.DataFrame(data)
   
    # View the first few rows
    print("First few rows:")
    print(df.head())
   
    # View the last few rows
    print("\nLast few rows:")
    print(df.tail())
   

   This is probably the 1st thing anyone does on receiving a dataset.

2. The Summary:

   info and describe

    # Get information about the DataFrame
    print("DataFrame info:")
    print(df.info())
   
    # Get summary statistics for numerical columns
    print("\nSummary statistics:")
    print(df.describe())
   

3. Miscelaneous:

    import pandas as pd
   
    # Create a sample DataFrame
    data = {
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
    }
    df = pd.DataFrame(data)
   
    # Get the values of the DataFrame as a NumPy array
    print("DataFrame values:")
    print(df.values)
   
    # Get the counts of unique values
    print("Value counts:")
    print(df['Name'].value_counts())
   
    # Get the unique values in the 'City' column
    print("Unique cities:")
    print(df['City'].unique())
   
    # Inspect data types of columns
    print("Data types of columns:")
    print(df.dtypes)
   

Cleaning the Data

1. The Null Detector:

    import pandas as pd
   
    # Create a sample DataFrame with missing values
    data = {
        'Name': ['Alice', 'Bob', 'Charlie', None, 'Emily'],
        'Age': [25, None, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', None]
    }
    df = pd.DataFrame(data)
   
    # Detect missing values
    print("Missing values:")
    print(df.isnull())
   

2. The Null Remover:

    # Drop rows with any missing values
    print("Drop rows with any missing values:")
    print(df.dropna())
   
    # Drop columns with any missing values
    print("Drop columns with any missing values:")
    print(df.dropna(axis=1))
   

3. The Null Filler:

    # Fill missing values with a specified value
    print("Fill missing values with 0:")
    print(df.fillna(0))
   
    # Forward fill missing values
    print("Forward fill missing values:")
    print(df.fillna(method='ffill'))
   

4. The Convertor:

   Convert data types of columns.

    # Convert the 'Age' column to integers
    print("Convert 'Age' column to integers:")
    df['Age'] = df['Age'].fillna(0)  # Fill missing values first
    print(df['Age'].astype(int))
   

5. The Doglapan Detector:**

   Doglapan aka**duplicates**

    # Create a sample DataFrame with duplicates
    data = {
        'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Emily'],
        'Age': [25, 30, 35, 25, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'New York', 'Miami']
    }
    df = pd.DataFrame(data)
   
    # Detect duplicate rows
    print("Duplicate rows:")
    print(df.duplicated())
   

6. The Doglapan Remover:**

    # Drop duplicate rows
    print("Drop duplicate rows:")
    print(df.drop_duplicates())
   

Manupulating the Data

1. The filter:

    import pandas as pd
   
    # Sample DataFrame
    data = {
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
    }
    df = pd.DataFrame(data)
   
    # Filter rows where Age is greater than 30
    filtered_df = df[df['Age'] > 30]
    print("Filtered DataFrame:\n", filtered_df)
   

2. The sorted method:

    # Sort by Age in ascending order
    sorted_df = df.sort_values(by='Age')
    print("DataFrame sorted by Age:\n", sorted_df)
   
    # Sort by index in descending order
    sorted_index_df = df.sort_index(ascending=False)
    print("\nDataFrame sorted by index:\n", sorted_index_df)
   

3. Adding & Removing Columns:

    # Add a new column
    df['Salary'] = [70000, 80000, 90000, 100000, 110000]
    print("DataFrame with new column:\n", df)
   
    # Remove a column
    df = df.drop(columns=['City'])
    print("\nDataFrame after removing column:\n", df)
   

4. The Aggregator:

    # Aggregation using sum
    sum_df = df.groupby('Name').sum()
    print("Sum Aggregation:\n", sum_df)
   
    # Aggregation using mean
    mean_df = df.groupby('Name').mean()
    print("\nMean Aggregation:\n", mean_df)
   
    # Aggregation using count
    count_df = df.groupby('Name').count()
    print("\nCount Aggregation:\n", count_df)
   

Transforming the Data

1. apply and map :

    import pandas as pd
   
    # Sample DataFrame
    df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})
   
    # Applying a function to each element of a column
    df['A_squared'] = df['A'].apply(lambda x: x ** 2)
   
    # Mapping values of a column to new values
    df['B_mapped'] = df['B'].map({5: 'Five', 6: 'Six', 7: 'Seven', 8: 'Eight'})
   
    print(df)
   

2. The Vectors:

    # Vectorized addition of two columns
    df['A_plus_B'] = df['A'] + df['B']
   
    print(df)
   

Conclusion

So, that's all folks. These are the 20 important Pandas (sounds weird 😂).

Keep coding, keep learning, and enjoy the endless possibilities that Python has to offer!

That's all folks. Leave a like and some lovely critics in the comments😁.

Signing off!!!👋