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`‎tutorials/things_in_pandas.ipynb‎`

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+39-1Lines changed: 39 additions & 1 deletion

Original file line number	Diff line number	Diff line change
`@@ -29,7 +29,7 @@`
`29`	`29`	`"output_type": "stream",`
`30`	`30`	`"stream": "stdout",`
`31`	`31`	`"text": [`
`32`		`- "Sebastian Raschka 25/01/2015 \n",`
	`32`	`+ "Sebastian Raschka 28/01/2015 \n",`
`33`	`33`	`"\n",`
`34`	`34`	`"CPython 3.4.2\n",`
`35`	`35`	`"IPython 2.3.1\n",`
`@@ -90,6 +90,7 @@`
`90`	`90`	`" - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",`
`91`	`91`	`" - [Adding a New Column](#Adding-a-New-Column)\n",`
`92`	`92`	`"- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",`
	`93`	`+ " - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n",`
`93`	`94`	`" - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",`
`94`	`95`	`" - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n",`
`95`	`96`	`" - [Filling NaN Rows](#Filling-NaN-Rows)\n",`
`@@ -937,6 +938,43 @@`
`937`	`938`	`"<br>"`
`938`	`939`	`]`
`939`	`940`	`},`
	`941`	`+ {`
	`942`	`+ "cell_type": "heading",`
	`943`	`+ "level": 3,`
	`944`	`+ "metadata": {},`
	`945`	`+ "source": [`
	`946`	`+ "Counting Rows with NaNs"`
	`947`	`+ ]`
	`948`	`+ },`
	`949`	`+ {`
	`950`	`+ "cell_type": "code",`
	`951`	`+ "collapsed": false,`
	`952`	`+ "input": [`
	`953`	`+ "nans = df.shape[0] - df.dropna().shape[0]\n",`
	`954`	`+ "\n",`
	`955`	`+ "print('%d rows have missing values' % nans)"`
	`956`	`+ ],`
	`957`	`+ "language": "python",`
	`958`	`+ "metadata": {},`
	`959`	`+ "outputs": [`
	`960`	`+ {`
	`961`	`+ "output_type": "stream",`
	`962`	`+ "stream": "stdout",`
	`963`	`+ "text": [`
	`964`	`+ "3 rows have missing values\n"`
	`965`	`+ ]`
	`966`	`+ }`
	`967`	`+ ],`
	`968`	`+ "prompt_number": 8`
	`969`	`+ },`
	`970`	`+ {`
	`971`	`+ "cell_type": "markdown",`
	`972`	`+ "metadata": {},`
	`973`	`+ "source": [`
	`974`	`+ "<br>\n",`
	`975`	`+ "<br>"`
	`976`	`+ ]`
	`977`	`+ },`
`940`	`978`	`{`
`941`	`979`	`"cell_type": "heading",`
`942`	`980`	`"level": 3,`

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