{"id":12376,"date":"2020-02-20T10:28:33","date_gmt":"2020-02-20T04:58:33","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/python-pandas-tutorial\/"},"modified":"2025-06-27T20:55:01","modified_gmt":"2025-06-27T15:25:01","slug":"python-pandas-tutorial","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/python-pandas-tutorial\/","title":{"rendered":"Introduction to Python Pandas"},"content":{"rendered":"\n

You work with data, and this data comes in tables, like in Excel spreadsheets or databases. Managing this kind of data in Python can be tricky with just basic lists or dictionaries. This is where Pandas helps.<\/p>\n\n\n\n

It helps you quickly handle common data tasks. This saves you a lot of time when preparing data for analysis<\/a> or machine learning<\/a>.<\/p>\n\n\n\n

What is Pandas?<\/h2>\n\n\n\n

Pandas is an open-source Python library. It provides data structures like Series<\/strong> and DataFrames<\/strong> that simplify working with \"relational\" or \"labeled\" data. You can think of it like a spreadsheet for your Python code.<\/p>\n\n\n\n

It builds on top of NumPy<\/a>, another Python library for numerical operations. Pandas makes data operations fast and efficient, even with large datasets.<\/p>\n\n\n\n

Getting Started: Installation and Import<\/h2>\n\n\n\n

Before you use Pandas, you need to install it. If you have Python installed, use pip<\/code>.<\/p>\n\n\n\n

To install Pandas, open your terminal or command prompt and type:<\/p>\n\n\n

\npip install pandas\n<\/pre><\/div>\n\n\n
After installing, you need to import the library into your Python script or Jupyter Notebook. The standard way is to import it with the alias pd<\/code>.<\/p>\n\n\n
\nimport pandas as pd\n<\/pre><\/div>\n\n\n
This line makes all Pandas functions available through pd.<\/code>.<\/p>\n\n\n\n    
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Pandas Data Structures<\/h2>\n\n\n\n
Pandas mainly uses two core data structures:<\/p>\n\n\n\n
    \n
  • Series:<\/strong> A one-dimensional array. It can hold any data type (numbers, strings, objects). Think of it as a single column of data from a spreadsheet.<\/li>\n\n\n\n
  • DataFrame:<\/strong> A two-dimensional table. It stores data in rows and columns. This is like a complete Excel spreadsheet or a database table.<\/li>\n<\/ul>\n\n\n\n
    Let\u2019s look at how to create them.<\/p>\n\n\n\n
    1. Creating a Series<\/h3>\n\n\n\n
    You can create a Series from a list or a NumPy array.<\/p>\n\n\n
    \nimport pandas as pd\n\n# Create a Series from a list\ndata_list = [10, 20, 30, 40, 50]\nmy_series = pd.Series(data_list)\nprint("Series from a list:")\nprint(my_series)\n\n# Create a Series with custom labels (index)\ndata_dict = {'a': 100, 'b': 200, 'c': 300}\nmy_series_labeled = pd.Series(data_dict)\nprint("\\nSeries with custom labels:")\nprint(my_series_labeled)\n<\/pre><\/div>\n\n\n
    This code outputs:<\/p>\n\n\n
    \nSeries from a list:\n0    10\n1    20\n2    30\n3    40\n4    50\ndtype: int64\n\nSeries with custom labels:\na    100\nb    200\nc    300\ndtype: int64\n<\/pre><\/div>\n\n\n
    Notice the numbers 0, 1, 2, 3, 4 on the left. These are the default index (labels) of the Series. When you use a dictionary, the keys become the index.<\/p>\n\n\n\n
    2. Creating a DataFrame<\/h3>\n\n\n\n
    You can create a DataFrame from dictionaries, lists of lists, or by reading data from files.<\/p>\n\n\n\n
    From a Dictionary<\/h4>\n\n\n\n
    This is a common way to create a DataFrame. Each key in the dictionary becomes a column name.<\/p>\n\n\n
    \nimport pandas as pd\n\n# Create a DataFrame from a dictionary\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n    'Age': [25, 30, 35, 28],\n    'City': ['New York', 'London', 'Paris', 'Tokyo']\n}\ndf = pd.DataFrame(data)\nprint("DataFrame from a dictionary:")\nprint(df)\n<\/pre><\/div>\n\n\n
    This code outputs:<\/p>\n\n\n
    \nDataFrame from a dictionary:\n      Name  Age      City\n0    Alice   25  New York\n1      Bob   30    London\n2  Charlie   35     Paris\n3    David   28     Tokyo\n<\/pre><\/div>\n\n\n
    This looks like a table, with column names and rows. The numbers 0, 1, 2, 3 are the default row index.<\/p>\n\n\n\n
    From a CSV File<\/h4>\n\n\n\n
    Often, your data comes from files like CSV (Comma Separated Values). Pandas has functions to read these files directly into a DataFrame.<\/p>\n\n\n\n
    First, create a sample data.csv<\/code> file:<\/p>\n\n\n
    \nName,Age,City\nAlice,25,New York\nBob,30,London\nCharlie,35,Paris\nDavid,28,Tokyo\n<\/pre><\/div>\n\n\n
    Now, read it into a DataFrame:<\/p>\n\n\n
    \nimport pandas as pd\n\n# Read a CSV file into a DataFrame\n# Make sure 'data.csv' is in the same directory as your script\ndf_csv = pd.read_csv('data.csv')\nprint("\\nDataFrame from a CSV file:")\nprint(df_csv)\n<\/pre><\/div>\n\n\n
    This code will produce the same DataFrame as the dictionary example if data.csv<\/code> is correctly set up.<\/p>\n\n\n\n
    Basic DataFrame Operations<\/h2>\n\n\n\n
    Once you have a DataFrame, you can do many things with it.<\/p>\n\n\n\n
    1. Viewing Data<\/h3>\n\n\n\n
    You often want to quickly see what your data looks like.<\/p>\n\n\n\n
      \n
    • df.head()<\/code>: Shows the first 5 rows.<\/li>\n\n\n\n
    • df.tail()<\/code>: Shows the last 5 rows.<\/li>\n\n\n\n
    • df.info()<\/code>: Gives a summary of the DataFrame, including data types and non-null values.<\/li>\n\n\n\n
    • df.describe()<\/code>: Provides statistical summary for numerical columns (mean, min, max, etc.).<\/li>\n\n\n\n
    • df.shape<\/code>: Returns a tuple with the number of rows and columns.<\/li>\n<\/ul>\n\n\n
      \nimport pandas as pd\n\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],\n    'Age': [25, 30, 35, 28, 22, 40],\n    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Rome', 'Berlin'],\n    'Salary': [50000, 60000, 75000, 55000, 48000, 80000]\n}\ndf = pd.DataFrame(data)\n\nprint("First 3 rows:")\nprint(df.head(3))\n\nprint("\\nLast 2 rows:")\nprint(df.tail(2))\n\nprint("\\nDataFrame Info:")\ndf.info()\n\nprint("\\nStatistical Description:")\nprint(df.describe())\n\nprint("\\nDataFrame Shape (rows, columns):")\nprint(df.shape)\n<\/pre><\/div>\n\n\n
      2. Selecting Data<\/h3>\n\n\n\n
      You can select specific columns or rows from your DataFrame.<\/p>\n\n\n\n
        \n
      • Select a single column: Use single square brackets df['ColumnName']<\/code>. This returns a Series.<\/li>\n\n\n\n
      • Select multiple columns: Use double square brackets df[['Col1', 'Col2']]<\/code>. This returns a DataFrame.<\/li>\n\n\n\n
      • Select rows by label (.loc<\/code>): Use .loc[row_label, column_label]<\/code>.<\/li>\n\n\n\n
      • Select rows by position (.iloc<\/code>): Use .iloc[row_position, column_position]<\/code>.<\/li>\n<\/ul>\n\n\n
        \nimport pandas as pd\n\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],\n    'Age': [25, 30, 35, 28, 22, 40],\n    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Rome', 'Berlin'],\n    'Salary': [50000, 60000, 75000, 55000, 48000, 80000]\n}\ndf = pd.DataFrame(data)\n\nprint("Select 'Name' column:")\nprint(df['Name'])\n\nprint("\\nSelect 'Name' and 'Age' columns:")\nprint(df[['Name', 'Age']])\n\nprint("\\nSelect row with index 1 (Bob) using .loc:")\nprint(df.loc[1]) # Selects row by its label (index)\n\nprint("\\nSelect row at position 1 (second row) using .iloc:")\nprint(df.iloc[1]) # Selects row by its integer position\n\nprint("\\nSelect 'City' for row with index 2 (Charlie) using .loc:")\nprint(df.loc[2, 'City'])\n\nprint("\\nSelect 'Age' for row at position 0 (Alice) using .iloc:")\nprint(df.iloc[0, 1]) # 0 for row position, 1 for column position (Age)\n<\/pre><\/div>\n\n\n
        3. Filtering Data<\/h3>\n\n\n\n
        You can filter rows based on conditions. This helps you select subsets of your data.<\/p>\n\n\n
        \nimport pandas as pd\n\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],\n    'Age': [25, 30, 35, 28, 22, 40],\n    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Rome', 'Berlin'],\n    'Salary': [50000, 60000, 75000, 55000, 48000, 80000]\n}\ndf = pd.DataFrame(data)\n\n# Filter for people older than 30\nfiltered_df = df[df['Age'] > 30]\nprint("People older than 30:")\nprint(filtered_df)\n\n# Filter for people from London\nlondon_df = df[df['City'] == 'London']\nprint("\\nPeople from London:")\nprint(london_df)\n\n# Filter with multiple conditions (Age > 25 AND Salary > 50000)\n# Use &amp; for AND, | for OR\nmulti_cond_df = df[(df['Age'] > 25) &amp; (df['Salary'] > 50000)]\nprint("\\nPeople older than 25 AND earning over 50000:")\nprint(multi_cond_df)\n<\/pre><\/div>\n\n\n
        4. Adding New Columns<\/h3>\n\n\n\n
        You can create new columns based on existing ones.<\/p>\n\n\n
        \nimport pandas as pd\n\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n    'Age': [25, 30, 35, 28],\n    'Salary': [50000, 60000, 75000, 55000]\n}\ndf = pd.DataFrame(data)\n\n# Add a new column 'Bonus'\ndf['Bonus'] = df['Salary'] * 0.10 # 10% bonus\nprint("DataFrame with 'Bonus' column:")\nprint(df)\n\n# Add a 'Salary_Category' column based on conditions\ndf['Salary_Category'] = ['High' if x > 60000 else 'Low' for x in df['Salary']]\nprint("\\nDataFrame with 'Salary_Category' column:")\nprint(df)\n<\/pre><\/div>\n\n\n
        Cleaning Data (Missing Values)<\/h2>\n\n\n\n
        Real-world data often has missing values. Pandas represents these as NaN<\/code> (Not a Number). You can handle them in a few ways.<\/p>\n\n\n\n
          \n
        • df.isnull()<\/code>: Returns a DataFrame of booleans, True<\/code> where values are missing.<\/li>\n\n\n\n
        • df.dropna()<\/code>: Removes rows or columns with missing values.<\/li>\n\n\n\n
        • df.fillna(value)<\/code>: Fills missing values with a specified value.<\/li>\n<\/ul>\n\n\n
          \nimport pandas as pd\nimport numpy as np # Used for NaN\n\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n    'Age': [25, 30, np.nan, 28, 22],\n    'City': ['New York', 'London', 'Paris', np.nan, 'Rome'],\n    'Salary': [50000, 60000, 75000, 55000, np.nan]\n}\ndf_missing = pd.DataFrame(data)\nprint("Original DataFrame with missing values:")\nprint(df_missing)\n\nprint("\\nCheck for null values:")\nprint(df_missing.isnull())\n\n# Drop rows with any missing values\ndf_cleaned_drop = df_missing.dropna()\nprint("\\nDataFrame after dropping rows with missing values:")\nprint(df_cleaned_drop)\n\n# Fill missing 'Age' with the mean age\nmean_age = df_missing['Age'].mean()\ndf_filled_age = df_missing.copy() # Make a copy to avoid changing original\ndf_filled_age['Age'] = df_filled_age['Age'].fillna(mean_age)\nprint(f"\\nDataFrame after filling missing Age with mean ({mean_age:.2f}):")\nprint(df_filled_age)\n\n# Fill missing 'City' with 'Unknown'\ndf_filled_city = df_missing.copy()\ndf_filled_city['City'] = df_filled_city['City'].fillna('Unknown')\nprint("\\nDataFrame after filling missing City with 'Unknown':")\nprint(df_filled_city)\n<\/pre><\/div>\n\n\n
          Grouping and Aggregating Data<\/h2>\n\n\n\n
          You often need to group data by a category and then calculate sums, averages, or counts for each group. Pandas groupby()<\/code> method is perfect for this.<\/p>\n\n\n
          \nimport pandas as pd\n\ndata = {\n    'Department': ['HR', 'Sales', 'HR', 'IT', 'Sales', 'IT'],\n    'Employee': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],\n    'Salary': [50000, 60000, 75000, 80000, 55000, 90000]\n}\ndf_employees = pd.DataFrame(data)\nprint("Original Employee Data:")\nprint(df_employees)\n\n# Group by 'Department' and calculate the mean salary\navg_salary_by_dept = df_employees.groupby('Department')['Salary'].mean()\nprint("\\nAverage Salary by Department:")\nprint(avg_salary_by_dept)\n\n# Group by 'Department' and count employees\nemployee_count_by_dept = df_employees.groupby('Department')['Employee'].count()\nprint("\\nEmployee Count by Department:")\nprint(employee_count_by_dept)\n\n# Group by 'Department' and get multiple aggregations\nagg_results = df_employees.groupby('Department').agg(\n    Total_Salary=('Salary', 'sum'),\n    Avg_Salary=('Salary', 'mean'),\n    Num_Employees=('Employee', 'count')\n)\nprint("\\nAggregated Results by Department:")\nprint(agg_results)\n<\/pre><\/div>\n\n\n
          Conclusion<\/h2>\n\n\n\n
          Pandas is an essential library for anyone working with data in Python. It provides flexible Series<\/strong> and DataFrames<\/strong> to handle tabular data.<\/p>\n\n\n\n
          You learned how to:<\/p>\n\n\n\n
            \n
          • Install Pandas and import it.<\/li>\n\n\n\n
          • Create Series and DataFrames from lists, dictionaries, and CSV files.<\/li>\n\n\n\n
          • View, select, and filter data using various methods like head()<\/code>, loc[]<\/code>, and boolean indexing.<\/li>\n\n\n\n
          • Add new columns and handle missing values.<\/li>\n\n\n\n
          • Group and aggregate data using groupby()<\/code>.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
            Pandas is a powerful Python library built for data manipulation and analysis. It gives you easy-to-use data structures and tools to work with tabular data. This means you can clean, transform, analyze, and visualize large datasets without writing a lot of complex code.<\/p>\n","protected":false},"author":41,"featured_media":12377,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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