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Updated median with only numeric values
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@ -404,7 +404,7 @@
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"outputs": [],
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"source": [
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"#Mean Age and SibSp of passengers grouped by passenger class and sex\n",
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"df.groupby(['Pclass', 'Sex'])['Age','SibSp'].mean()"
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"df.groupby(['Pclass', 'Sex'])[['Age','SibSp']].mean()"
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]
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},
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{
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@ -414,7 +414,7 @@
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"outputs": [],
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"source": [
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"#Show mean Age and SibSp for passengers older than 25 grouped by Passenger Class and Sex\n",
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"df[df.Age > 25].groupby(['Pclass', 'Sex'])['Age','SibSp'].mean()"
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"df[df.Age > 25].groupby(['Pclass', 'Sex'])[['Age','SibSp']].mean()"
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]
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},
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{
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@ -424,7 +424,7 @@
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"outputs": [],
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"source": [
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"# Mean age, SibSp , Survived of passengers older than 25 which survived, grouped by Passenger Class and Sex \n",
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"df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])['Age','SibSp','Survived'].mean()"
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"df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].mean()"
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]
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},
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{
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@ -436,7 +436,7 @@
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"# We can also decide which function apply in each column\n",
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"\n",
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"#Show mean Age, mean SibSp, and number of passengers older than 25 that survived, grouped by Passenger Class and Sex\n",
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"df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])['Age','SibSp','Survived'].agg({'Age': np.mean, \n",
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"df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].agg({'Age': np.mean, \n",
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" 'SibSp': np.mean, 'Survived': np.sum})"
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]
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},
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@ -600,8 +600,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fill missing values with the median\n",
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"df_filled = df.fillna(df.median())\n",
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"# Fill missing values with the median, we avoid empty (None) values with numeric_only\n",
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"df_filled = df.fillna(df.median(numeric_only=True))\n",
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"df_filled[-5:]"
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]
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},
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@ -685,7 +685,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# But we are working on a copy \n",
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"# But we are working on a copy, so we get a warning\n",
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"df.iloc[889]['Sex'] = np.nan"
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]
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},
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@ -695,7 +695,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# If we want to change, we should not chain selections\n",
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"# If we want to change it, we should not chain selections\n",
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"# The selection can be done with the column name\n",
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"df.loc[889, 'Sex']"
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]
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@ -932,11 +932,11 @@
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"metadata": {},
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"source": [
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"* [Pandas](http://pandas.pydata.org/)\n",
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"* [Learning Pandas, Michael Heydt, Packt Publishing, 2015](http://proquest.safaribooksonline.com/book/programming/python/9781783985128)\n",
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"* [Useful Pandas Snippets](https://gist.github.com/bsweger/e5817488d161f37dcbd2)\n",
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"* [Pandas. Introduction to Data Structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro)\n",
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"* [Learning Pandas, Michael Heydt, Packt Publishing, 2017](https://learning.oreilly.com/library/view/learning-pandas/9781787123137/)\n",
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"* [Pandas. Introduction to Data Structures](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html)\n",
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"* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
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"* [Boolean Operators in Pandas](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-operators)"
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"* [Boolean Operators in Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-operators)\n",
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"* [Useful Pandas Snippets](https://gist.github.com/bsweger/e5817488d161f37dcbd2)"
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]
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},
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{
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@ -958,7 +958,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -972,7 +972,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.8.12"
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},
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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