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Find Rolling Mean – Python Pandas
<p>To find rolling mean, we will use the apply() function in Pandas. At first, let us import the required library −</p><pre class="just-code notranslate language-python" data-lang="python">import pandas as pd</pre><p>Create a DataFrame with 2 columns. One is an int column −</p><pre class="just-code notranslate language-python" data-lang="python" style="">dataFrame = pd.DataFrame( { "Car": ['Tesla', 'Mercedes', 'Tesla', 'Mustang', 'Mercedes', 'Mustang'], "Reg_Price": [5000, 1500, 6500, 8000, 9000, 6000] } )</pre><p>Group using GroupBy and find the Rolling Mean using apply() −</p><pre class="just-code notranslate language-python" data-lang="python" style="">dataFrame.groupby("Car")["Reg_Price"].apply( lambda x: x.rolling(center=False, window=2).mean()) </pre><h2>Example</h2><p>Following is the code −</p><pre class="demo-code notranslate language-python" data-lang="python" style="">import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "Car": ['Tesla', 'Mercedes', 'Tesla', 'Mustang', 'Mercedes', 'Mustang'], "Reg_Price": [5000, 1500, 6500, 8000, 9000, 6000] } ) print"DataFrame ... ",dataFrame print" Rolling Mean... ",dataFrame.groupby("Car")["Reg_Price"].apply( lambda x: x.rolling(center=False, window=2).mean()) </pre><h2 style="">Output</h2><p>This will produce the following output −</p><pre class="result notranslate">DataFrame ... Car Reg_Price 0 Tesla 5000 1 Mercedes 1500 2 Tesla 6500 3 Mustang 8000 4 Mercedes 9000 5 Mustang 6000 Rolling Mean... 0 NaN 1 NaN 2 5750.0 3 NaN 4 5250.0 5 7000.0 Name: Reg_Price, dtype: float64</pre>
Updated on 17-Sep-2021 07:30:12
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