diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..82c421e 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -44,10 +44,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0 5.7\n", + "1 75.2\n", + "2 74.4\n", + "3 84.0\n", + "4 66.5\n", + "5 66.3\n", + "6 55.8\n", + "7 75.7\n", + "8 29.1\n", + "9 43.7\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_1 = pd.Series(lst)\n", + "series_1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['T', 'AXISLEN', 'AXISORDERS', 'AXISTOAXISNUMBER', 'HANDLEDTYPES', 'abs', 'add', 'and', 'annotations', 'array', 'arraypriority', 'arrayufunc', 'bool', 'class', 'columnconsortiumstandard', 'contains', 'copy', 'deepcopy', 'delattr', 'delitem', 'dict', 'dir', 'divmod', 'doc', 'eq', 'finalize', 'float', 'floordiv', 'format', 'ge', 'getattr', 'getattribute', 'getitem', 'getstate', 'gt', 'hash', 'iadd', 'iand', 'ifloordiv', 'imod', 'imul', 'init', 'initsubclass', 'int', 'invert', 'ior', 'ipow', 'isub', 'iter', 'itruediv', 'ixor', 'le', 'len', 'lt', 'matmul', 'mod', 'module', 'mul', 'ne', 'neg', 'new', 'nonzero', 'or', 'pandaspriority', 'pos', 'pow', 'radd', 'rand', 'rdivmod', 'reduce', 'reduceex', 'repr', 'rfloordiv', 'rmatmul', 'rmod', 'rmul', 'ror', 'round', 'rpow', 'rsub', 'rtruediv', 'rxor', 'setattr', 'setitem', 'setstate', 'sizeof', 'str', 'sub', 'subclasshook', 'truediv', 'weakref', 'xor', 'accessors', 'accumfunc', 'aggexamplesdoc', 'aggseealsodoc', 'alignforop', 'alignframe', 'alignseries', 'append', 'arithmethod', 'asmanager', 'attrs', 'binop', 'canholdna', 'checkinplaceandallowsduplicatelabels', 'checkischainedassignmentpossible', 'checklabelorlevelambiguity', 'checksetitemcopy', 'clearitemcache', 'clipwithonebound', 'clipwithscalar', 'cmpmethod', 'consolidate', 'consolidateinplace', 'constructaxesdict', 'constructresult', 'constructor', 'constructorexpanddim', 'constructorexpanddimfrommgr', 'constructorfrommgr', 'convertdtypes', 'data', 'deprecatedowncast', 'diradditions', 'dirdeletions', 'dropaxis', 'droplabelsorlevels', 'duplicated', 'expanddimfrommgr', 'findvalidindex', 'flags', 'flexmethod', 'frommgr', 'getaxis', 'getaxisname', 'getaxisnumber', 'getaxisresolvers', 'getblockmanageraxis', 'getbooldata', 'getcacher', 'getcleanedcolumnresolvers', 'getindexresolvers', 'getlabelorlevelvalues', 'getnumericdata', 'getrowswithmask', 'getvalue', 'getvaluestuple', 'getwith', 'getitemslice', 'gotitem', 'hiddenattrs', 'indexedsame', 'infoaxis', 'infoaxisname', 'infoaxisnumber', 'initdict', 'initmgr', 'inplacemethod', 'internalnames', 'internalnamesset', 'iscached', 'iscopy', 'islabelorlevelreference', 'islabelreference', 'islevelreference', 'ismixedtype', 'isview', 'itemcache', 'ixs', 'logicalfunc', 'logicalmethod', 'mapvalues', 'maybeupdatecacher', 'memoryusage', 'metadata', 'mgr', 'mincountstatfunction', 'name', 'needsreindexmulti', 'padorbackfill', 'protectconsolidate', 'reduce', 'references', 'reindexaxes', 'reindexindexer', 'reindexmulti', 'reindexwithindexers', 'rename', 'replacesingle', 'reprdataresource', 'reprlatex', 'resetcache', 'resetcacher', 'setascached', 'setaxis', 'setaxisname', 'setaxisnocheck', 'setiscopy', 'setlabels', 'setname', 'setvalue', 'setvalues', 'setwith', 'setwithengine', 'shiftwithfreq', 'slice', 'statfunction', 'statfunctionddof', 'takewithiscopy', 'tolatexviastyler', 'typ', 'updateinplace', 'validatedtype', 'values', 'where', 'abs', 'add', 'addprefix', 'addsuffix', 'agg', 'aggregate', 'align', 'all', 'any', 'apply', 'argmax', 'argmin', 'argsort', 'array', 'asfreq', 'asof', 'astype', 'at', 'attime', 'attrs', 'autocorr', 'axes', 'backfill', 'between', 'betweentime', 'bfill', 'bool', 'clip', 'combine', 'combinefirst', 'compare', 'convertdtypes', 'copy', 'corr', 'count', 'cov', 'cummax', 'cummin', 'cumprod', 'cumsum', 'describe', 'diff', 'div', 'divide', 'divmod', 'dot', 'drop', 'dropduplicates', 'droplevel', 'dropna', 'dtype', 'dtypes', 'duplicated', 'empty', 'eq', 'equals', 'ewm', 'expanding', 'explode', 'factorize', 'ffill', 'fillna', 'filter', 'first', 'firstvalidindex', 'flags', 'floordiv', 'ge', 'get', 'groupby', 'gt', 'hasnans', 'head', 'hist', 'iat', 'idxmax', 'idxmin', 'iloc', 'index', 'inferobjects', 'info', 'interpolate', 'ismonotonicdecreasing', 'ismonotonicincreasing', 'isunique', 'isin', 'isna', 'isnull', 'item', 'items', 'keys', 'kurt', 'kurtosis', 'last', 'lastvalidindex', 'le', 'loc', 'lt', 'map', 'mask', 'max', 'mean', 'median', 'memoryusage', 'min', 'mod', 'mode', 'mul', 'multiply', 'name', 'nbytes', 'ndim', 'ne', 'nlargest', 'notna', 'notnull', 'nsmallest', 'nunique', 'pad', 'pctchange', 'pipe', 'plot', 'pop', 'pow', 'prod', 'product', 'quantile', 'radd', 'rank', 'ravel', 'rdiv', 'rdivmod', 'reindex', 'reindexlike', 'rename', 'renameaxis', 'reorderlevels', 'repeat', 'replace', 'resample', 'resetindex', 'rfloordiv', 'rmod', 'rmul', 'rolling', 'round', 'rpow', 'rsub', 'rtruediv', 'sample', 'searchsorted', 'sem', 'setaxis', 'setflags', 'shape', 'shift', 'size', 'skew', 'sortindex', 'sortvalues', 'squeeze', 'std', 'sub', 'subtract', 'sum', 'swapaxes', 'swaplevel', 'tail', 'take', 'toclipboard', 'tocsv', 'todict', 'toexcel', 'toframe', 'tohdf', 'tojson', 'tolatex', 'tolist', 'tomarkdown', 'tonumpy', 'toperiod', 'topickle', 'tosql', 'tostring', 'totimestamp', 'toxarray', 'transform', 'transpose', 'truediv', 'truncate', 'tzconvert', 'tzlocalize', 'unique', 'unstack', 'update', 'valuecounts', 'values', 'var', 'view', 'where', 'xs']\n" + ] + } + ], + "source": [ + "print([i.replace(\"_\", \"\") for i in dir(series_1)])" + ] }, { "cell_type": "markdown", @@ -60,10 +101,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_1[2]" + ] }, { "cell_type": "markdown", @@ -74,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +146,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Score_1 Score_3 Score_5\n", + "0 53.1 67.5 78.4\n", + "1 61.3 30.8 87.6\n", + "2 20.6 44.2 91.8\n", + "3 57.4 96.1 69.5\n", + "4 83.6 85.4 35.9\n", + "5 49.0 0.1 89.1\n", + "6 23.3 95.0 26.9\n", + "7 27.6 53.8 68.5\n", + "8 96.6 53.4 50.1\n", + "9 73.7 43.2 34.7" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_subset = df_1[['Score_1', 'Score_3', 'Score_5']]\n", + "df_subset" + ] }, { "cell_type": "markdown", @@ -161,10 +598,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "56.95000000000001" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "av_score_3 = df_1['Score_3'].mean()\n", + "av_score_3" + ] }, { "cell_type": "markdown", @@ -175,10 +626,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "88.8" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_score_4 = df_1['Score_4'].max()\n", + "max_score_4" + ] }, { "cell_type": "markdown", @@ -189,10 +654,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "40.75" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "med_score_2 = df_1['Score_2'].median()\n", + "med_score_2" + ] }, { "cell_type": "markdown", @@ -203,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -224,10 +703,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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DescriptionQuantityUnitPriceRevenue
0LUNCH BAG APPLE DESIGN11.651.65
1SET OF 60 VINTAGE LEAF CAKE CASES240.5513.20
2RIBBON REEL STRIPES DESIGN11.651.65
3WORLD WAR 2 GLIDERS ASSTD DESIGNS28800.18518.40
4PLAYING CARDS JUBILEE UNION JACK21.252.50
5POPCORN HOLDER70.855.95
6BOX OF VINTAGE ALPHABET BLOCKS111.9511.95
7PARTY BUNTING44.9519.80
8JAZZ HEARTS ADDRESS BOOK100.191.90
9SET OF 4 SANTA PLACE SETTINGS481.2560.00
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" + ], + "text/plain": [ + " Description Quantity UnitPrice Revenue\n", + "0 LUNCH BAG APPLE DESIGN 1 1.65 1.65\n", + "1 SET OF 60 VINTAGE LEAF CAKE CASES 24 0.55 13.20\n", + "2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65\n", + "3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.40\n", + "4 PLAYING CARDS JUBILEE UNION JACK 2 1.25 2.50\n", + "5 POPCORN HOLDER 7 0.85 5.95\n", + "6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n", + "7 PARTY BUNTING 4 4.95 19.80\n", + "8 JAZZ HEARTS ADDRESS BOOK 10 0.19 1.90\n", + "9 SET OF 4 SANTA PLACE SETTINGS 48 1.25 60.00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2 = pd.DataFrame(orders)\n", + "df_2" + ] }, { "cell_type": "markdown", @@ -238,10 +841,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978\n" + ] + }, + { + "data": { + "text/plain": [ + "637.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_q = df_2.Quantity.sum()\n", + "total_rev = df_2.Revenue.sum()\n", + "\n", + "print(total_q)\n", + "total_rev" + ] }, { "cell_type": "markdown", @@ -252,10 +879,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "11.95" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "most_exp = df_2.UnitPrice.max()\n", + "most_exp" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.18" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "least_exp = df_2.UnitPrice.min()\n", + "least_exp" + ] }, { "cell_type": "markdown", @@ -266,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +947,144 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1231610433.03.58.0010.72
2332211033.52.58.6710.80
3431410322.03.08.2100.65
4533011554.53.09.3410.90
5632110933.04.08.2010.75
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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "0 1 337 118 4 4.5 4.5 9.65 \n", + "1 2 316 104 3 3.0 3.5 8.00 \n", + "2 3 322 110 3 3.5 2.5 8.67 \n", + "3 4 314 103 2 2.0 3.0 8.21 \n", + "4 5 330 115 5 4.5 3.0 9.34 \n", + "5 6 321 109 3 3.0 4.0 8.20 \n", + "\n", + " Research Chance of Admit \n", + "0 1 0.92 \n", + "1 1 0.72 \n", + "2 1 0.80 \n", + "3 0 0.65 \n", + "4 1 0.90 \n", + "5 1 0.75 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head(6)" + ] }, { "cell_type": "markdown", @@ -299,31 +1095,305 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Serial No. 0\n", + "GRE Score 0\n", + "TOEFL Score 0\n", + "University Rating 0\n", + "SOP 0\n", + "LOR 0\n", + "CGPA 0\n", + "Research 0\n", + "Chance of Admit 0\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.isna(admissions).sum()" + ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 23, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "385" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### 2 - Interestingly, there is a column that uniquely identifies the applicants. This column is the serial number column. Instead of having our own index, we should make this column our index. Do this in the cell below. Keep the column in the dataframe in addition to making it an index." + "admissions.shape[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Serial No. 385\n", + "GRE Score 385\n", + "TOEFL Score 385\n", + "University Rating 385\n", + "SOP 385\n", + "LOR 385\n", + "CGPA 385\n", + "Research 385\n", + "Chance of Admit 385\n", + "dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.notna(admissions).sum()" + ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### 2 - Interestingly, there is a column that uniquely identifies the applicants. This column is the serial number column. Instead of having our own index, we should make this column our index. Do this in the cell below. Keep the column in the dataframe in addition to making it an index." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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385 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "0 1 337 118 4 4.5 4.5 9.65 \n", + "1 2 316 104 3 3.0 3.5 8.00 \n", + "2 3 322 110 3 3.5 2.5 8.67 \n", + "3 4 314 103 2 2.0 3.0 8.21 \n", + "4 5 330 115 5 4.5 3.0 9.34 \n", + ".. ... ... ... ... ... ... ... \n", + "380 381 324 110 3 3.5 3.5 9.04 \n", + "381 382 325 107 3 3.0 3.5 9.11 \n", + "382 383 330 116 4 5.0 4.5 9.45 \n", + "383 384 312 103 3 3.5 4.0 8.78 \n", + "384 385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit Decision decision2 \n", + "0 1 0.92 True 1 \n", + "1 1 0.72 True 0 \n", + "2 1 0.80 True 1 \n", + "3 0 0.65 True 0 \n", + "4 1 0.90 True 1 \n", + ".. ... ... ... ... \n", + "380 1 0.82 True 1 \n", + "381 1 0.84 True 0 \n", + "382 1 0.91 True 1 \n", + "383 0 0.67 True 1 \n", + "384 1 0.95 True 1 \n", + "\n", + "[385 rows x 11 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition = admissions['SOP'] > 3\n", + "admissions['decision2'] = np.where(condition, 1, 0)\n", + "admissions" + ] } ], "metadata": { @@ -449,7 +2323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "toc": { "base_numbering": "",