{"id":7445,"date":"2021-05-30T09:15:44","date_gmt":"2021-05-30T03:45:44","guid":{"rendered":"https:\/\/python-programs.com\/?p=7445"},"modified":"2021-11-22T18:53:35","modified_gmt":"2021-11-22T13:23:35","slug":"pandas-create-dataframe-from-list-of-dictionaries","status":"publish","type":"post","link":"https:\/\/python-programs.com\/pandas-create-dataframe-from-list-of-dictionaries\/","title":{"rendered":"Pandas: Create Dataframe from List of Dictionaries"},"content":{"rendered":"<h2>Methods of creating a dataframe from a list of dictionaries<\/h2>\n<p>In this article, we discuss different methods by which we can create a dataframe from a list of dictionaries. Before going to the actual article let us done some observations that help to understand the concept easily. Suppose we have a list of dictionary:-<\/p>\n<p><code>list_of_dict = [<br \/>\n{'Name': 'Mayank' , 'Age': 25, 'Marks': 91},<br \/>\n{'Name': 'Raj', 'Age': 21, 'Marks': 97},<br \/>\n{'Name': 'Rahul', 'Age': 23, 'Marks': 79},<br \/>\n{'Name': 'Manish' , 'Age': 23},<br \/>\n]<\/code><\/p>\n<p>Here we know that dictionaries consist of key-value pairs. So we can analyze that if we make the key as our column name and values as the column value then a dataframe is easily created. And we have a list of dictionaries so a dataframe with multiple rows also.<\/p>\n<h3>pandas.DataFrame<\/h3>\n<p>This methods helps us to create dataframe in python<\/p>\n<p><code>syntax: <span class=\"enlighter-text\">pandas.<\/span><span class=\"enlighter-m1\">DataFrame<\/span><span class=\"enlighter-g1\">(<\/span><span class=\"enlighter-text\">data=<\/span><span class=\"enlighter-e1\">None<\/span><span class=\"enlighter-text\">, index=<\/span><span class=\"enlighter-e1\">None<\/span><span class=\"enlighter-text\">, columns=<\/span><span class=\"enlighter-e1\">None<\/span><span class=\"enlighter-text\">, dtype=<\/span><span class=\"enlighter-e1\">None<\/span><span class=\"enlighter-text\">, copy=<\/span><span class=\"enlighter-e0\">False<\/span><span class=\"enlighter-g1\">)<\/span><\/code><\/p>\n<h3>Let us see different methods to create dataframe from a list of dictionaries<\/h3>\n<ul>\n<li>\n<h3>Method 1-Create Dataframe from list of dictionaries with default indexes<\/h3>\n<\/li>\n<\/ul>\n<p>As we see in in pandas.Datframe() method there is parameter name data.We have to simply pass our list of dictionaries in this method and it will return the dataframe.Let see this with the help of an example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict)\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">   Age  Marks    Name\r\n0   25     91  Mayank\r\n1   21     97     Raj\r\n2   23     79   Rahul\r\n3   23     86  Manish<\/span><\/pre>\n<p>Here we see that dataframe is created with default indexes 0,1,2,3&#8230;.<\/p>\n<p>Now a question may arise if from any dictionary key-value pair is less than other dictionaries.So in this case what happened.Let understand it with the help of an example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict)\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">    Age  Marks    Name\r\n0  25.0   91.0  Mayank\r\n1  21.0   97.0     Raj\r\n2   NaN   79.0   Rahul\r\n3  23.0    NaN  Manish<\/span><\/pre>\n<p>Here we see in case of missing key value pair NaN value is there in the output.<\/p>\n<ul>\n<li>\n<h3>Method 2- Create Dataframe from list of dictionary with custom indexes<\/h3>\n<\/li>\n<\/ul>\n<p>Unlike the previous method where we have default indexes we can also give custom indexes by passes list of indexes in index parameter of pandas.DataFrame() function.Let see this with the help of an example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict,index=['a','b','c','d'])\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">    Age  Marks    Name\r\na  25.0   91.0  Mayank\r\nb  21.0   97.0     Raj\r\nc   NaN   79.0   Rahul\r\nd  23.0    NaN  Manish<\/span><\/pre>\n<p>Here we see that instead of default index 1,2,3&#8230;.. we have now indes a,b,c,d.<\/p>\n<ul>\n<li>\n<h3>Method 3-Create Dataframe from list of dictionaries with changed order of columns<\/h3>\n<\/li>\n<\/ul>\n<p>With the help of pandas.DataFrame() method we can easily arrange order of column by simply passes list ozf columns in columns parameter in the order in which we want to display it in our dataframe.Let see this with the help of example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict,columns=['Name', 'Marks', 'Age'])\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">     Name  Marks  Age\r\n0  Mayank     91   25\r\n1     Raj     97   21\r\n2   Rahul     79   23\r\n3  Manish     86   23<\/span><\/pre>\n<p>Here also a question may arise if we pass less column in columns parameter or we pass more column in parameter then what happened.Let see this with the help of an example.<\/p>\n<h3>Case 1: Less column in column parameter<\/h3>\n<p>In this case the column which we don&#8217;t pass will be drop from the dataframe.Let see this with the help of an example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict,columns=['Name', 'Marks'])\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">     Name  Marks\r\n0  Mayank     91\r\n1     Raj     97\r\n2   Rahul     79\r\n3  Manish     86<\/span><\/pre>\n<p>Here we see that we didn&#8217;t pass Age column that&#8217;s why Age clumn is also not in our dataframe.<\/p>\n<h3>Case 2: More column in column parameter<\/h3>\n<p>In this case a new column will be added in dataframe but its all the value will be NaN.Let see this with the help of an example.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as pd\r\nimport numpy as np\r\n\r\nlist_of_dict = [\r\n    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},\r\n    {'Name': 'Raj',  'Age': 21,  'Marks': 97},\r\n    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},\r\n    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},\r\n]\r\n#create dataframe\r\ndf=pd.DataFrame(list_of_dict,columns=['Name', 'Marks', 'Age','city'])\r\nprint(df)<\/pre>\n<p>Output<\/p>\n<pre class=\"jqconsole jqconsole-blurred\"><span class=\"\">     Name  Marks  Age  city\r\n0  Mayank     91   25   NaN\r\n1     Raj     97   21   NaN\r\n2   Rahul     79   23   NaN\r\n3  Manish     86   23   NaN<\/span><\/pre>\n<p>So these are the methods to create dataframe from list of dictionary in pandas.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Methods of creating a dataframe from a list of dictionaries In this article, we discuss different methods by which we can create a dataframe from a list of dictionaries. Before going to the actual article let us done some observations that help to understand the concept easily. Suppose we have a list of dictionary:- list_of_dict &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/pandas-create-dataframe-from-list-of-dictionaries\/\"> <span class=\"screen-reader-text\">Pandas: Create Dataframe from List of Dictionaries<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[5,6],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas: Create Dataframe from List of Dictionaries - Python Programs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/python-programs.com\/pandas-create-dataframe-from-list-of-dictionaries\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: Create Dataframe from List of Dictionaries - Python Programs\" \/>\n<meta property=\"og:description\" content=\"Methods of creating a dataframe from a list of dictionaries In this article, we discuss different methods by which we can create a dataframe from a list of dictionaries. 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