{"id":8110,"date":"2021-02-17T22:47:50","date_gmt":"2021-02-17T17:17:50","guid":{"rendered":"https:\/\/pynative.com\/?p=8110"},"modified":"2023-03-09T11:50:42","modified_gmt":"2023-03-09T06:20:42","slug":"pandas-dataframe-from-python-dict","status":"publish","type":"post","link":"https:\/\/pynative.com\/pandas-dataframe-from-python-dict\/","title":{"rendered":"Create Pandas DataFrame from Python dictionary"},"content":{"rendered":"\n<p>This article shows how to convert a <a href=\"https:\/\/pynative.com\/python-dictionaries\/\">Python dictionary<\/a> to <a href=\"https:\/\/pynative.com\/python-pandas-dataframe\/\">pandas DataFrame<\/a>. It covers the creating DataFrame from all types of dictionaries using the DataFrame constructor and <code>from_dict()<\/code> method.<\/p>\n\n\n\n<p>And at the end of this article, we summarize the usage of both ways with the comparison. So stay tuned&#8230;<\/p>\n\n\n\n<p>Python dictionary is the data structure that stores the data in key-value pairs. By converting data from dictionary format to DataFrame will make it very competent for analysis by using functions of DataFrame.<\/p>\n\n\n\n<p>There are multiple ways to convert Python dictionary object into Pandas DataFrame. Majorly used ways are,<\/p>\n\n\n\n<ol>\n<li>DataFrame constructor<\/li>\n\n\n\n<li><code>from_dict()<\/code><\/li>\n<\/ol>\n\n\n\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Table of contents<\/h2><ul><li><a href=\"#h-create-dataframe-from-dict-using-constructor\" data-level=\"2\">Create DataFrame from dict using constructor<\/a><\/li><li><a href=\"#h-dataframe-from-dict-with-required-columns-only\" data-level=\"2\">DataFrame from dict with required columns only<\/a><\/li><li><a href=\"#h-dataframe-from-dict-with-user-defined-indexes\" data-level=\"2\">DataFrame from dict with&nbsp;user-defined indexes<\/a><\/li><li><a href=\"#h-dataframe-from-dict-by-changing-the-column-data-type\" data-level=\"2\">DataFrame from dict by changing the column data type<\/a><\/li><li><a href=\"#h-dataframe-from-dict-with-a-single-value\" data-level=\"2\">DataFrame from dict with a single value<\/a><\/li><li><a href=\"#h-dataframe-from-dict-with-key-and-value-as-a-column\" data-level=\"2\">DataFrame from dict with key and value as a column<\/a><\/li><li><a href=\"#h-create-dataframe-from-list-of-dict\" data-level=\"2\">Create DataFrame from list of dict<\/a><\/li><li><a href=\"#h-the-from_dict-function\" data-level=\"2\">The from_dict() function<\/a><\/li><li><a href=\"#h-dataframe-from-dict-with-dict-keys-as-a-row\" data-level=\"2\">DataFrame from dict with dict keys as a row<\/a><\/li><li><a href=\"#h-dataframe-from-dict-where-values-are-variable-length-lists\" data-level=\"2\">DataFrame from dict where values are variable-length lists<\/a><\/li><li><a href=\"#h-dataframe-from-dict-nested-dict\" data-level=\"2\">DataFrame from dict nested dict<\/a><\/li><li><a href=\"#h-dataframe-constructor-vs-from_dict\" data-level=\"2\">DataFrame constructor vs from_dict()<\/a><\/li><\/ul><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-create-dataframe-from-dict-using-constructor\">Create DataFrame from dict using constructor<\/h2>\n\n\n\n<p><a href=\"https:\/\/pynative.com\/python-pandas-dataframe\/#h-dataframe-constructor\">DataFrame constructor<\/a> can be used to create DataFrame from different data structures in python like <code>dict<\/code>, list, set, tuple, and <code>ndarray<\/code>.<\/p>\n\n\n\n<p>In the below example, we create a DataFrame object using dictionary objects contain student data. <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"444\" height=\"354\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/pandas-dataframe-from-dictionary.png\" alt=\"Pandas DataFrame from Python Dictionary\" class=\"wp-image-8361\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/pandas-dataframe-from-dictionary.png 444w, https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/pandas-dataframe-from-dictionary-300x239.png 300w\" sizes=\"(max-width: 444px) 100vw, 444px\" \/><\/figure><\/div>\n\n\n<p>When you convert a <code>dict<\/code> to DataFrame by default,&nbsp; all the keys of the <code>dict <\/code>object becomes columns, and the range of numbers 0, 1, 2,&#8230;,n is assigned as a row index.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code1\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-number\">19<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>, <span class=\"hljs-number\">91.54<\/span>]}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code1', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code1');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'name': ['Joe', 'Nat', 'Harry'], 'age': [20, 21, 19], 'marks': [85.1, 77.8, 91.54]}\n\n    name  age  marks\n0    Joe   20  85.10\n1    Nat   21  77.80\n2  Harry   19  91.54<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-with-required-columns-only\">DataFrame from dict with required columns only<\/h2>\n\n\n\n<p>While converting the whole <code>dict<\/code> to DataFrame, we may need only some of the columns to be included in the resulting DataFrame.<\/p>\n\n\n\n<p>We can select only required columns by passing list column labels to  <code>columns=['col1', 'col2']<\/code> parameter in the constructor.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the case of student DataFrame for analyzing the annual score, we need only &#8220;<strong>student name<\/strong>&#8221; and &#8220;<strong>marks<\/strong>&#8221; whereas the &#8220;<strong>age<\/strong>&#8221; column is not required. We can select only required columns, as shown in the below example.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code2\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-number\">19<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>, <span class=\"hljs-number\">91.54<\/span>]}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict, columns=&#91;<span class=\"hljs-string\">\"name\"<\/span>, <span class=\"hljs-string\">\"marks\"<\/span>])\nprint(student_df)\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code2', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code2');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'name': ['Joe', 'Nat', 'Harry'], 'age': [20, 21, 19], 'marks': [85.1, 77.8, 91.54]}\n\n    name  marks\n0    Joe  85.10\n1    Nat  77.80\n2  Harry  91.54<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-with-user-defined-indexes\">DataFrame from dict with&nbsp;user-defined indexes<\/h2>\n\n\n\n<p>In pandas DataFrame, each row has an index that is used to identify each row. In some cases, we need to provide a customized index for each row. We can do that while creating the DataFrame from <code>dict<\/code> using the <code>index<\/code> parameter of the DataFrame constructor.<\/p>\n\n\n\n<p>The default index is a range of integers starting from 0 to a number of rows. We can pass a list of the row indexes as <code>index=['index1','index2'<\/code>] to the dataFrame constructor.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we have given a customer index for each student, making it more readable and easy to access the row using it.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code3\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-3\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-comment\"># import pandas library<\/span>\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-number\">19<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>, <span class=\"hljs-number\">91.54<\/span>]}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict, index=&#91;<span class=\"hljs-string\">\"stud1\"<\/span>, <span class=\"hljs-string\">\"stud2\"<\/span>, <span class=\"hljs-string\">\"stud3\"<\/span>])\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-3\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code3', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code3');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'name': ['Joe', 'Nat', 'Harry'], 'age': [20, 21, 19], 'marks': [85.1, 77.8, 91.54]}\n\n        name  age  marks\nstud1    Joe   20  85.10\nstud2    Nat   21  77.80\nstud3  Harry   19  91.54<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-by-changing-the-column-data-type\">DataFrame from dict by changing the column data type<\/h2>\n\n\n\n<p>By default, while creating a DataFrame from <code>dict<\/code> using constructor, it keeps the original data type of the values in dict. But, if we need to change the data type of the data in the resulting DataFrame, we can use the <code>dtype <\/code>parameter in the constructor.<\/p>\n\n\n\n<p>Only one data type is allowed to specify as <code>dtype='data_type'<\/code> which will be applicable for all the data in the resultant DataFrame. If we do not force such a data type, it internally infers from the Data.<\/p>\n\n\n\n<p><strong>Note:<\/strong> It changes the data type only if it is compatible with the new data type. Otherwise, it keeps the original data type.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>As you can see below example, we are trying to change the data type to <strong>float64 <\/strong>for all the columns. But, it changes the data type of &#8220;<strong>age<\/strong>&#8221; and &#8220;<strong>marks<\/strong>&#8221; columns only to <strong>float64 <\/strong>even though the &#8220;<strong>marks<\/strong>&#8221; column type was &#8220;<strong>object<\/strong>&#8220;. But, the &#8220;<strong>name<\/strong>&#8221; column type is not changed because string values in that column cannot be converted to <strong>float64<\/strong>.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code4\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-4\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-comment\"># import pandas library<\/span>\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-number\">19<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-string\">\"85\"<\/span>, <span class=\"hljs-string\">\"77\"<\/span>, <span class=\"hljs-string\">\"91.54\"<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(<span class=\"hljs-string\">\"DataFrame with inferred data type : \\n\"<\/span>, student_df.dtypes)\n\nstudent_df = pd.DataFrame(student_dict, dtype=<span class=\"hljs-string\">\"float64\"<\/span>)\nprint(<span class=\"hljs-string\">\"DataFrame with changed data type : \\n\"<\/span>, student_df.dtypes)\n\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-4\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code4', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code4');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">DataFrame with inferred data type : \nname     object\nage      int64\nmarks    object\ndtype: object\n\nDataFrame with changed data type : \nname     object\nage      float64\nmarks    float64\ndtype: object\n\n    name   age  marks\n0    Joe  20.0  85.00\n1    Nat  21.0  77.00\n2  Harry  19.0  91.54<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-with-a-single-value\">DataFrame from dict with a single value<\/h2>\n\n\n\n<p>If we have a <code>dict<\/code> with only single values for each key and need to convert such <code>dict<\/code> to the DataFrame, we can use the DataFrame constructor.<\/p>\n\n\n\n<p>In such a case, it converts the <code>dict<\/code> to DataFrame as we have seen before, like keys of the <code>dict<\/code> will be column labels and values will be the column data. But, we must provide the index parameter to give the row index. Else it throws an error,<\/p>\n\n\n\n<p><code>ValueError: If using all scalar values, you must pass an index<\/code><\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we have provided the customized <code>index=['stud1']<\/code> to the DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code5\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-5\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-comment\"># import pandas library<\/span>\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">'name'<\/span>: <span class=\"hljs-string\">'Smith'<\/span>, <span class=\"hljs-string\">'age'<\/span>: <span class=\"hljs-number\">22<\/span>, <span class=\"hljs-string\">'marks'<\/span>: <span class=\"hljs-number\">88.90<\/span>}\nprint(student_dict)\n\nstudent_df = pd.DataFrame(student_dict, index=&#91;<span class=\"hljs-string\">'stud1'<\/span>])\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-5\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code5', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code5');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'name': 'Smith', 'age': 22, 'marks': 88.9}\n\n       name  age  marks\nstud1  Smith   22   88.9<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-with-key-and-value-as-a-column\">DataFrame from dict with key and value as a column<\/h2>\n\n\n\n<p>Suppose we have a dictionary object where the key is the <strong>student&#8217;s name<\/strong>, and the value is the student&#8217;s marks. And we want the keys in one column and all the values in another column of the DataFrame.<\/p>\n\n\n\n<p>For that, rather than passing a whole <code>dict<\/code> object, we need to pass each key-value pair in the dictionary to the DataFrame constructor to create a new DataFrame. <\/p>\n\n\n\n<p>We can get the entry of key-value pair using <code>dict.items()<\/code> and pass that function to the constructor.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>As shown in the below example, we need to pass an entry of key-value to the constructor and give column labels using <code>columns<\/code> parameter.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code6\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-6\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"Joe\"<\/span>: <span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>: <span class=\"hljs-number\">75.83<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>: <span class=\"hljs-number\">69.70<\/span>}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict.items(), columns=&#91;<span class=\"hljs-string\">\"name\"<\/span>, <span class=\"hljs-string\">\"marks\"<\/span>])\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-6\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code6', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code6');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'Joe': 85.1, 'Nat': 75.83, 'Harry': 69.7}\n\n    name  marks\n0    Joe  85.10\n1    Nat  75.83\n2  Harry  69.70<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-create-dataframe-from-list-of-dict\">Create DataFrame from list of dict<\/h2>\n\n\n\n<p>For the sake of our understanding, consider the case where each school stores data of students into the dictionary data structure. Each school store different information about students. Like, some school stores student&#8217;s hobby whereas some school only stores academic information. If we want to analyze data of all the students from the city, we need to gather all this information into the DataFrame.<\/p>\n\n\n\n<p>To convert such a list of <code>dict<\/code> from different schools can be converted to a single DataFrame using either <code>DataFrame.from_dict()<\/code> function or DataFrame constructor.<\/p>\n\n\n\n<p>By default, keys of all the different dictionary objects are converted into columns of resultant DataFrame. It handles the missing keys by adding NaN where the values for the column are missing.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Let&#8217;s see how we can use a constructor to create DataFrame from different dictionary objects.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code7\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-7\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = &#91;{<span class=\"hljs-string\">\"name\"<\/span>: <span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"age\"<\/span>: <span class=\"hljs-number\">20<\/span>, <span class=\"hljs-string\">\"marks\"<\/span>: <span class=\"hljs-string\">\"85.58\"<\/span>, <span class=\"hljs-string\">\"hobby\"<\/span>: <span class=\"hljs-string\">\"smimming\"<\/span>},\n                {<span class=\"hljs-string\">\"name\"<\/span>: <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"age\"<\/span>: <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-string\">\"marks\"<\/span>: <span class=\"hljs-string\">\"77.21\"<\/span>, },\n                {<span class=\"hljs-string\">\"name\"<\/span>: <span class=\"hljs-string\">\"Harry\"<\/span>, <span class=\"hljs-string\">\"age\"<\/span>: <span class=\"hljs-number\">19<\/span>, <span class=\"hljs-string\">\"marks\"<\/span>: <span class=\"hljs-string\">\"91.54\"<\/span>}]\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame object<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-7\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code7', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code7');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">[{'name': 'Joe', 'age': 20, 'marks': '85.58', 'hobby': 'smimming'}, {'name': 'Nat', 'age': 21, 'marks': '77.21'}, {'name': 'Harry', 'age': 19, 'marks': '91.54'}]\n\n    name  age  marks     hobby\n0    Joe   20  85.58  smimming\n1    Nat   21  77.21       NaN\n2  Harry   19  91.54       NaN<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-the-from_dict-function\">The <code>from_dict()<\/code> function<\/h2>\n\n\n\n<p>This is another way of creating DataFrame from a Python dictionary using <code>DataFrame.from_dict()<\/code> method.<\/p>\n\n\n\n<p><strong>Note:<\/strong> This method is useful for the cases when you need to <strong>transpose<\/strong> the DataFrame i.e. when we need the keys in the dictionary object as rows in the resultant DataFrame. In all the other cases DataFrame constructor should be preferred.<\/p>\n\n\n<pre id=\"syntax\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-8\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">DataFrame.from_dict(data, orient=<span class=\"hljs-string\">'columns'<\/span>, dtype=<span class=\"hljs-literal\">None<\/span>, columns=<span class=\"hljs-literal\">None<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-8\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<ol>\n<li><code>data<\/code>: It takes <code>dict<\/code>, <code>list<\/code>, <code>set<\/code>, <code>ndarray<\/code>, <code>Iterable<\/code>, or <code>DataFrame<\/code> as input. An empty DataFrame will be created if it is not provided. The resultant column order follows the insertion order.<\/li>\n\n\n\n<li><code>orient<\/code>: (Optional) If the keys of the <code>dict<\/code> should be the rows of the DataFrame, then set <code>orient = index<\/code> else set it to <code>column<\/code> (Default) if the keys should be columns of the resultant DataFrame.<\/li>\n\n\n\n<li><code>dtype <\/code>: (Optional) data type to force on resulting DataFrame. Only a single data type is allowed. If not given, then it&#8217;s inferred from the data.<\/li>\n\n\n\n<li><code>columns <\/code>: (Optional) <strong>Only be used in case of <code>orient=\"index\"<\/code><\/strong> to specify column labels in the resulting DataFrame. Default column labels are range of integer i.e. 0,1,2\u2026n. <strong>Note:<\/strong> If we use the <code>columns<\/code> parameter with <code class=\"docutils literal notranslate\"><span class=\"pre\">orient='columns'<\/span><\/code> it throws an <code>ValueError: cannot use columns parameter with orient='columns'<\/code><\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-with-dict-keys-as-a-row\">DataFrame from dict with dict keys as a row<\/h2>\n\n\n\n<p>It is used to transpose the DataFrame, i.e., when keys in the dictionary should be the rows in the resultant DataFrame. We can change the orientation of the DataFrame using a parameter <code>orient=\"index\"<\/code> in <code>DataFrame.from_dict()<\/code>.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, keys &#8220;<strong>name<\/strong>&#8220;, &#8220;<strong>age<\/strong>&#8220;, and &#8220;<strong>marks<\/strong>&#8221; becomes row indexes in the DataFrame, and values are added in respective rows. New column labels are provided using <code>columns<\/code> parameter.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code8\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-9\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>, <span class=\"hljs-number\">19<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>, <span class=\"hljs-number\">91.54<\/span>]}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame.from_dict(student_dict, orient=<span class=\"hljs-string\">\"index\"<\/span>, columns=&#91;<span class=\"hljs-string\">\"stud1\"<\/span>, <span class=\"hljs-string\">\"stud2\"<\/span>, <span class=\"hljs-string\">\"stud3\"<\/span>])\nprint(student_df)\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-9\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code8', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code8');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'name': ['Joe', 'Nat', 'Harry'], 'age': [20, 21, 19], 'marks': [85.1, 77.8, 91.54]}\n\n      stud1 stud2  stud3\nname    Joe   Nat  Harry\nage      20    21     19\nmarks  85.1  77.8  91.54<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-where-values-are-variable-length-lists\">DataFrame from dict where values are variable-length lists<\/h2>\n\n\n\n<p>It is a widespread use case in the IT industry where data is stored in the dictionary with different values against each key.<\/p>\n\n\n\n<p>If such a dictionary object needs to be converted into the DataFrame such that keys and values will be added as columns in DataFrame. Then it can be done using&nbsp;chaining of <code>DataFrame.from_dict()<\/code>, <code>stack()<\/code>, and <code>reset_index()<\/code> functions.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Here, we have <code>dict<\/code> with values are of different sizes and still we need to add all the key-values into a DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code9\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-10\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create dict object<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"Grade A\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Harry\"<\/span>], <span class=\"hljs-string\">\"Grade B\"<\/span>: &#91;<span class=\"hljs-string\">\"Nat\"<\/span>]}\nprint(student_dict)\n\nstudent_df = pd.DataFrame.from_dict(student_dict, <span class=\"hljs-string\">'index'<\/span>).stack().reset_index(level=<span class=\"hljs-number\">0<\/span>)\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-10\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code9', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code9');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'Grade A': ['Joe', 'Harry'], 'Grade B': ['Nat']}\n\n   level_0      0\n0  Grade A    Joe\n1  Grade A  Harry\n0  Grade B    Nat<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-from-dict-nested-dict\">DataFrame from dict nested dict<\/h2>\n\n\n\n<p>In this section, we cover the complex structure of the dictionary object where we have a hierarchical structure of the dictionary i.e. one dictionary object into another dictionary object.<\/p>\n\n\n\n<p>In the below example, we have a student dictionary object where student data categorized by their grades and further divided as per their class. Such a dictionary object is converted into the multi-index DataFrame using <code>DataFrame.from_dict()<\/code> by iterating over each key and its values and parameter <code>orient='index'<\/code>.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code10\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-11\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># Create hierarchical dict<\/span>\nstudent_dict = {<span class=\"hljs-string\">\"Grade A\"<\/span>: {<span class=\"hljs-string\">'Class A'<\/span>: {<span class=\"hljs-string\">'name'<\/span>: <span class=\"hljs-string\">'Joe'<\/span>, <span class=\"hljs-string\">'marks'<\/span>: <span class=\"hljs-number\">91.56<\/span>},\n                            <span class=\"hljs-string\">'Class B'<\/span>: {<span class=\"hljs-string\">'name'<\/span>: <span class=\"hljs-string\">'Harry'<\/span>, <span class=\"hljs-string\">'marks'<\/span>: <span class=\"hljs-number\">87.90<\/span>}},\n                <span class=\"hljs-string\">\"Grade B\"<\/span>: {<span class=\"hljs-string\">'Class A'<\/span>: {<span class=\"hljs-string\">'name'<\/span>: <span class=\"hljs-string\">'Sam'<\/span>, <span class=\"hljs-string\">'marks'<\/span>: <span class=\"hljs-number\">70<\/span>},\n                            <span class=\"hljs-string\">'Class B'<\/span>: {<span class=\"hljs-string\">'name'<\/span>: <span class=\"hljs-string\">'Alan'<\/span>, <span class=\"hljs-string\">'marks'<\/span>: <span class=\"hljs-number\">65.48<\/span>}}}\nprint(student_dict)\n\n<span class=\"hljs-comment\"># Create multi-index DataFrame<\/span>\nstudent_df = pd.DataFrame.from_dict({(i, j): student_dict&#91;i]&#91;j]\n                                     <span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> student_dict.keys()\n                                     <span class=\"hljs-keyword\">for<\/span> j <span class=\"hljs-keyword\">in<\/span> student_dict&#91;i].keys()},\n                                    orient=<span class=\"hljs-string\">'index'<\/span>)\nprint(student_df)\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-11\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code10', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code10');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">{'Grade A': {'Class A': {'name': 'Joe', 'marks': 91.56}, 'Class B': {'name': 'Harry', 'marks': 87.9}}, 'Grade B': {'Class A': {'name': 'Sam', 'marks': 70}, 'Class B': {'name': 'Alan', 'marks': 65.48}}}\n\n                  name  marks\nGrade A Class A    Joe  91.56\n        Class B  Harry  87.90\nGrade B Class A    Sam  70.00\n        Class B   Alan  65.48<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-dataframe-constructor-vs-from_dict\">DataFrame constructor vs <code>from_dict()<\/code><\/h2>\n\n\n\n<p>The below table summarizes all the cases of converting <code>dict<\/code> to the DataFrame that we have already discussed in this article. It shows the comparison of using the DataFrame constructor and <code>DataFrame.from_dict()<\/code> method.<\/p>\n\n\n\n<p>It will help you to choose the correct function for converting the <code>dict<\/code> to the DataFrame.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table><thead><tr><th>Use Case<\/th><th>DataFrame <br>constructor<\/th><th>from_dict()<br>method<\/th><\/tr><\/thead><tbody><tr><td>Custom column names<\/td><td>Yes<\/td><td>No<\/td><\/tr><tr><td>custom index<\/td><td>Yes<\/td><td>No<\/td><\/tr><tr><td>dict with a single value<\/td><td>Yes<\/td><td>No<\/td><\/tr><tr><td>list of dict<\/td><td>Yes<\/td><td>Yes<\/td><\/tr><tr><td>handle missing keys<\/td><td>Yes<\/td><td>Yes<\/td><\/tr><tr><td>keys and values as columns<\/td><td>Yes<\/td><td>Yes<\/td><\/tr><tr><td>change datatype<\/td><td>Yes<\/td><td>Yes<\/td><\/tr><tr><td>Orient=column(Keys as columns)<\/td><td>Yes<\/td><td>Yes<\/td><\/tr><tr><td>Orient=index(Keys as rows)<\/td><td>No<\/td><td>Yes<\/td><\/tr><tr><td>Multi-index DataFrame<\/td><td>No<\/td><td>Yes<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>This article shows how to convert a Python dictionary to pandas DataFrame. It covers the creating DataFrame from all types of dictionaries using the DataFrame constructor and from_dict() method. And at the end of this article, we summarize the usage of both ways with the comparison. So stay tuned&#8230; Python dictionary is the data structure [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[21,22],"tags":[40],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.4 (Yoast SEO v22.4) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Create Pandas DataFrame from Python Dictionary<\/title>\n<meta name=\"description\" content=\"Learn to convert Python dictionary to pandas DataFrame. 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