{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ibrahim Salami",
  "title_listed": "The Absolute Beginner's Guide to Pandas DataFrames",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/the-absolute-beginners-guide-to-pandas-dataframes/",
  "publish_date": "2025-11-17",
  "corpus_stage": "",
  "notes": "",
  "id": "9a7bbb217829",
  "collected_at": "2026-10-02T12:09:37",
  "title": "The Absolute Beginner’s Guide to Pandas DataFrames | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# The Absolute Beginner’s Guide to Pandas DataFrames\nLearn how to initialize dataframes from dictionaries, lists, and NumPy arrays\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nNovember 17, 20255 min read\n![](../images/000.jpg)\nImage by author\nComing from a SQL background, learning Python for data analysis has been a bit challenging. The syntax is simpler — true. However, the language and terminology are completely different. In SQL, you’ll have to interact with databases, tables and columns. In Python, however, for data analysis, your bread and butter is going to be data structures.\nData structures in Python are like data storage objects. Python includes several built-in data structures, such as lists, tuples, sets, and dictionaries. All these are used to store and manipulate data. Some are mutable (lists) and some are not (tuples). To learn more about Python data structures, I highly recommend reading the book **\"Python for Data Analysis\" by Wes McKinney.** I just started reading it, and I think it’s stellar.\nIn this article, I’m going to walk you through what a DataFrame is in Pandas and how to create one step by step.\n## Understand Array fundamentals\nThere’s a library in Python called NumPy; [you might have heard of it](https://towardsdatascience.com/numpy-for-absolute-beginners-a-project-based-approach-to-data-analysis/). It’s mostly used for mathematical and numerical computations. One of the features it offers is the ability to create arrays. You might be wondering. What the heck is an Array?\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\nAn array is similar to a list, except it only stores values of the same data type. Lists, however, can store values of different data types (int, text, boolean, etc). Here’s an example of a list\npython\n\n```\nmy_list = [1, “hello”, 3.14, True]\n```\n\nLists are also mutable. In other words, you can add and remove elements.\nBack to arrays. In Numpy, Arrays can be multidimensional — this is called ndarrays (N-dimensional arrays). For instance, let’s import the Numpy library in Python.  \n\npython\n\n```\nimport numpy as np\n```\n\nTo create a basic array in Numpy, we use the `np.array()` function. In this function, our array is stored.\npython\n\n```\narr = np.array([1, 2, 3, 4, 5])\narr\n```\n\nHere’s the result:\npython\n\n```\narray([1, 2, 3, 4, 5])\n```\n\nTo check the data type.\npython\n\n```\ntype(arr)\n```\n\nWe’ll get the data type.\npython\n\n```\nnumpy.ndarray\n```\n\nThe cool thing about arrays is that you can perform mathematical calculations on them. For instance\npython\n\n```\narr*2\n```\n\nThe result:\npython\n\n```\narray([ 2, 4, 6, 8, 10])\n```\n\nPretty cool, right?\nNow that you know the basics of arrays in Numpy. Let’s dig deeper into N-dimensional arrays.\nThe array you see above is a 1-dimensional (1D) array. Also known as vector arrays, 1D arrays consist of a sequence of values. Like so, **[1,2,3,4,5]**\n**2-dimensional arrays (Matrix)** can store 1D arrays as the values. Similar to rows of a table in SQL, each 1D array is like one row of data. The output is like a grid of values. For instance:\npython\n\n```\nimport numpy as np\narr = np.array([[1, 2, 3], [4, 5, 6]])\narr\n```\n\nOutput:\nmarkup\n\n```\n[[1 2 3][4 5 6]]\n```\n\n**3-dimensional arrays (Tensors)** can store 2D arrays (matrices). For instance,\npython\n\n```\nimport numpy as np\narr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\narr\n```\n\n  \nOutput:\nmarkup\n\n```\n[[[1 2 3][4 5 6]][[1 2 3][4 5 6]]]\n```\n\nAn array can have an infinite number of dimensions, depending on the amount of data you want to store.\n## Creating a dataframe from an array\nNow that you’ve gotten the gist about Arrays. Let’s create a DataFrame from one.\nFirst, we’ll have to import the pandas and NumPy libraries\npython\n\n```\nimport pandas as pd\nimport numpy as np\n```\n\nNext, create our Array:\npython\n\n```\ndata = np.array([[1, 4], [2, 5], [3, 6]])\n```\n\nHere, I’ve created a 2D Array. Pandas DataFrame can only store 1D and 2D arrays. If you try to pass in a 3D Array, you’ll get an error.\nNow that we’ve got our Array. Let’s pass it into our DataFrame. To create a DataFrame, use the `pd.DataFrame() `function.\npython\n\n```\n# creating the DataFrame\ndf = pd.DataFrame(data)\n\n# showing the DataFrame\ndf\n```\n\n**Output**\nmarkup\n\n```\n0 10 1 41 2 52 3 6\n```\n\nLooking good so far. But it needs a little formatting:\npython\n\n```\n# creating a dataframe\ndf = pd.DataFrame(data, index=['row1', 'row2', 'row3'],\ncolumns=['col1', 'col2'])\n\n# showing the dataframe\ndf\n```\n\n**Output**\nmarkup\n\n```\ncol1 col2row1 1 4row2 2 5row3 3 6\n```\n\nNow that’s better. All I did was rename the rows using the index attribute and the columns using the columns attribute.\nAnd there you go, you have your DataFrame. It’s that simple. Let’s explore some more handy ways to create a DataFrame.\n## Creating a DataFrame from a dictionary\nOne of the built-in data structures Python offers is dictionaries. Basically, dictionaries are used to store key-value pairs, where all keys must be unique and immutable. It’s represented by curly brackets {}. Here’s an example of a dictionary:\n`dict = {\"name\": \"John\", \"age\": 30}`\nHere, the keys are name and age, and the values are Alice and 30. Simple as that. Now, let’s create a DataFrame from a dictionary.\npython\n\n```\nnames = [\"John\", \"David\", \"Jane\", \"Mary\"]\nage = [30, 27, 35, 23]\n```\n\nFirst, I created a list to store multiple names and ages:\npython\n\n```\ndict_names = {'Names': names, 'Age': age}\n```\n\nNext, I stored all the values in a dictionary and created keys for Names and Age.\npython\n\n```\n# Creating the dataframe\ndf_names = pd.DataFrame(dict_names)\ndf_names\n```\n\nAbove, we have our DataFrame storing the dictionary we created. Here’s the output below:\nmarkup\n\n```\nNames Age0 John 301 David 272 Jane 353 Mary 23\n```\n\nAnd there we go, we have a DataFrame created from a dictionary.\n## Creating a DataFrame from a CSV file\nThis is probably the method you’ll be using the most. It’s common practice to read CSV files in pandas when trying to do data analysis. Similar to how you open spreadsheets in Excel or import data to SQL. In Python, you read CSVs by using the `read_csv()` function. Here’s an example:\npython\n\n```\n# reading the csv file\ndf_exams = pd.read_csv('StudentsPerformance.csv')\n```\n\nIn some cases, you’ll have to copy the file path and paste it as:\npython\n\n```\npd.read_csv(“C:\\data\\suppliers lists — Sheet1.csv”)\n```\n\nOutput:\n![Image by Author\n](../images/001.jpg)\nAnd there you go!\n## Wrapping up\nCreating DataFrames in pandas might seem complex, but it actually isn’t. In most cases, you’ll probably be reading CSV files anyway. So don’t sweat it. I hope you found this article helpful. Would love to hear your thoughts in the comments. Thanks for reading!\nWanna connect? Feel free to say hi on these platforms\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BxTBPgk2fTXSRGVGhY8QDdQ%3D%3D)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n[Medium](https://medium.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# The Absolute Beginner’s Guide to Pandas DataFrames\nLearn how to initialize dataframes from dictionaries, lists, and NumPy arrays\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nNovember 17, 20255 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/11/pandas-dataframe.jpg)\nImage by author\nComing from a SQL background, learning Python for data analysis has been a bit challenging. The syntax is simpler — true. However, the language and terminology are completely different. In SQL, you’ll have to interact with databases, tables and columns. In Python, however, for data analysis, your bread and butter is going to be data structures.\nData structures in Python are like data storage objects. Python includes several built-in data structures, such as lists, tuples, sets, and dictionaries. All these are used to store and manipulate data. Some are mutable (lists) and some are not (tuples). To learn more about Python data structures, I highly recommend reading the book **\"Python for Data Analysis\" by Wes McKinney.** I just started reading it, and I think it’s stellar.\nIn this article, I’m going to walk you through what a DataFrame is in Pandas and how to create one step by step.\n## Understand Array fundamentals\nThere’s a library in Python called NumPy; [you might have heard of it](https://towardsdatascience.com/numpy-for-absolute-beginners-a-project-based-approach-to-data-analysis/). It’s mostly used for mathematical and numerical computations. One of the features it offers is the ability to create arrays. You might be wondering. What the heck is an Array?\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\nAn array is similar to a list, except it only stores values of the same data type. Lists, however, can store values of different data types (int, text, boolean, etc). Here’s an example of a list\npython\n\n```\nmy_list = [1, “hello”, 3.14, True]\n```\n\nLists are also mutable. In other words, you can add and remove elements.\nBack to arrays. In Numpy, Arrays can be multidimensional — this is called ndarrays (N-dimensional arrays). For instance, let’s import the Numpy library in Python.  \n\npython\n\n```\nimport numpy as np\n```\n\nTo create a basic array in Numpy, we use the `np.array()` function. In this function, our array is stored.\npython\n\n```\narr = np.array([1, 2, 3, 4, 5])\narr\n```\n\nHere’s the result:\npython\n\n```\narray([1, 2, 3, 4, 5])\n```\n\nTo check the data type.\npython\n\n```\ntype(arr)\n```\n\nWe’ll get the data type.\npython\n\n```\nnumpy.ndarray\n```\n\nThe cool thing about arrays is that you can perform mathematical calculations on them. For instance\npython\n\n```\narr*2\n```\n\nThe result:\npython\n\n```\narray([ 2, 4, 6, 8, 10])\n```\n\nPretty cool, right?\nNow that you know the basics of arrays in Numpy. Let’s dig deeper into N-dimensional arrays.\nThe array you see above is a 1-dimensional (1D) array. Also known as vector arrays, 1D arrays consist of a sequence of values. Like so, **[1,2,3,4,5]**\n**2-dimensional arrays (Matrix)** can store 1D arrays as the values. Similar to rows of a table in SQL, each 1D array is like one row of data. The output is like a grid of values. For instance:\npython\n\n```\nimport numpy as np\narr = np.array([[1, 2, 3], [4, 5, 6]])\narr\n```\n\nOutput:\nmarkup\n\n```\n[[1 2 3][4 5 6]]\n```\n\n**3-dimensional arrays (Tensors)** can store 2D arrays (matrices). For instance,\npython\n\n```\nimport numpy as np\narr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\narr\n```\n\n  \nOutput:\nmarkup\n\n```\n[[[1 2 3][4 5 6]][[1 2 3][4 5 6]]]\n```\n\nAn array can have an infinite number of dimensions, depending on the amount of data you want to store.\n## Creating a dataframe from an array\nNow that you’ve gotten the gist about Arrays. Let’s create a DataFrame from one.\nFirst, we’ll have to import the pandas and NumPy libraries\npython\n\n```\nimport pandas as pd\nimport numpy as np\n```\n\nNext, create our Array:\npython\n\n```\ndata = np.array([[1, 4], [2, 5], [3, 6]])\n```\n\nHere, I’ve created a 2D Array. Pandas DataFrame can only store 1D and 2D arrays. If you try to pass in a 3D Array, you’ll get an error.\nNow that we’ve got our Array. Let’s pass it into our DataFrame. To create a DataFrame, use the `pd.DataFrame() `function.\npython\n\n```\n# creating the DataFrame\ndf = pd.DataFrame(data)\n\n# showing the DataFrame\ndf\n```\n\n**Output**\nmarkup\n\n```\n0 10 1 41 2 52 3 6\n```\n\nLooking good so far. But it needs a little formatting:\npython\n\n```\n# creating a dataframe\ndf = pd.DataFrame(data, index=['row1', 'row2', 'row3'],\ncolumns=['col1', 'col2'])\n\n# showing the dataframe\ndf\n```\n\n**Output**\nmarkup\n\n```\ncol1 col2row1 1 4row2 2 5row3 3 6\n```\n\nNow that’s better. All I did was rename the rows using the index attribute and the columns using the columns attribute.\nAnd there you go, you have your DataFrame. It’s that simple. Let’s explore some more handy ways to create a DataFrame.\n## Creating a DataFrame from a dictionary\nOne of the built-in data structures Python offers is dictionaries. Basically, dictionaries are used to store key-value pairs, where all keys must be unique and immutable. It’s represented by curly brackets {}. Here’s an example of a dictionary:\n`dict = {\"name\": \"John\", \"age\": 30}`\nHere, the keys are name and age, and the values are Alice and 30. Simple as that. Now, let’s create a DataFrame from a dictionary.\npython\n\n```\nnames = [\"John\", \"David\", \"Jane\", \"Mary\"]\nage = [30, 27, 35, 23]\n```\n\nFirst, I created a list to store multiple names and ages:\npython\n\n```\ndict_names = {'Names': names, 'Age': age}\n```\n\nNext, I stored all the values in a dictionary and created keys for Names and Age.\npython\n\n```\n# Creating the dataframe\ndf_names = pd.DataFrame(dict_names)\ndf_names\n```\n\nAbove, we have our DataFrame storing the dictionary we created. Here’s the output below:\nmarkup\n\n```\nNames Age0 John 301 David 272 Jane 353 Mary 23\n```\n\nAnd there we go, we have a DataFrame created from a dictionary.\n## Creating a DataFrame from a CSV file\nThis is probably the method you’ll be using the most. It’s common practice to read CSV files in pandas when trying to do data analysis. Similar to how you open spreadsheets in Excel or import data to SQL. In Python, you read CSVs by using the `read_csv()` function. Here’s an example:\npython\n\n```\n# reading the csv file\ndf_exams = pd.read_csv('StudentsPerformance.csv')\n```\n\nIn some cases, you’ll have to copy the file path and paste it as:\npython\n\n```\npd.read_csv(“C:\\data\\suppliers lists — Sheet1.csv”)\n```\n\nOutput:\n![Image by Author\n](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/11/Screenshot-2025-11-14-152726.jpg)\nAnd there you go!\n## Wrapping up\nCreating DataFrames in pandas might seem complex, but it actually isn’t. In most cases, you’ll probably be reading CSV files anyway. So don’t sweat it. I hope you found this article helpful. Would love to hear your thoughts in the comments. Thanks for reading!\nWanna connect? Feel free to say hi on these platforms\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BxTBPgk2fTXSRGVGhY8QDdQ%3D%3D)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n[Medium](https://medium.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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