Here are the functions in the pandas’ module or library:<\/p>\n\n\n\n
import pandas as pd\n\nDATA FRAME\n<\/strong>data = {'Country': ['Belgium', 'India', 'Brazil'],\n\n'Capital': ['Brussels', 'New Delhi', 'Brasilia'],\n\n'Population': [11190846, 1303171035, 207847528]} \n\ndf = pd.DataFrame(data,columns=['Country', 'Capital', 'Population'])\n\nREAD AND WRITE CSV<\/strong>\n\npd.read_csv('file.csv', header=None, nrows=5)\ndf.to_csv('myDataFrame.csv')\n\nRead multiple sheets from the same file<\/strong>\n\nxlsx = pd.ExcelFile('file.xls')\ndf = pd.read_excel(xlsx, 'Sheet1')\n\nRead and Write to Excel<\/strong>\n\npd.read_excel('file.xlsx')\ndf.to_excel('dir\/myDataFrame.xlsx', sheet_name='Sheet1')\n\nRead and Write to SQL Query or Database Table\n<\/strong>(read_sql()is a convenience wrapper around read_sql_table() and read_sql_query())\n\nfrom sqlalchemy import create_engine\nengine = create_engine('sqlite:\/\/\/:memory:')\npd.read_sql(SELECT * FROM my_table;, engine)\npd.read_sql_table('my_table', engine)\npd.read_sql_query(SELECT * FROM my_table;', engine)\ndf.to_sql('myDf', engine)\n\n\nSelection\n<\/strong>Getting\nGet one element<\/em>\n\ns['b']\n-5\n\nGet subset of a DataFrame\n<\/strong>\ndf[1:]\nCountry Capital Population\n1 India New Delhi 1303171035\n2 Brazil Brasilia 207847528\n\n\nSelecting', Boolean Indexing and Setting\n<\/strong>By Position\nSelect single value by row and and column<\/em>\n\ndf.iloc([0], [0])\n'Belgium'\ndf.iat([0], [0])\n'Belgium'\n\nBy Label\n<\/strong>Select single value by row and column labels\n<\/em>\ndf.loc([0], ['Country'])\n'Belgium'\ndf.at([0], ['Country'])\n'Belgium'\n\nBy Label\/Position\n<\/strong>Select single row of subset of rows\n<\/em>\ndf.ix[2]\nCountry Brazil\nCapital Brasilia\nPopulation 207847528\n\nSelect a single column of subset of columns\n<\/strong>\ndf.ix[:, 'Capital']\n0 Brussels\n1 New Delhi\n2 Brasilia\n\nSelect rows and columns\n<\/strong>\ndf.ix[1, 'Capital']\n'New Delhi'\n\nBoolean Indexing\n<\/strong>Series s where value is not >1\n<\/em>\ns[~(s > 1)]\n\ns where value is <-1 or >2\n<\/em>\ns[(s < -1) | (s > 2)]\n\nUse filter to adjust DataFrame\n<\/strong>\ndf[df['Population']>1200000000]\nPOWERED BY DATACAMP WORKSPACE\n\nSet index a of Series s to 6\n<\/em>\ns['a'] = 6\n\nDropping\n<\/strong>Drop values from rows (axis=0)\n\ns.drop(['a', 'c'])\n\nDrop values from columns(axis=1)\n<\/em>\ndf.drop('Country', axis=1) \n\nSort and Rank\n<\/strong>Sort by labels along an axis\n<\/em>\ndf.sort_index()\n\nSort by the values along an axis\n<\/em>\ndf.sort_values(by='Country') \n\nAssign ranks to entries\n<\/strong>\ndf.rank()\n\nRetrieving Series\/DataFrame Information\n<\/strong>Basic Information\n(rows, columns)\n<\/em>\ndf.shape\n\nDescribe index\n<\/strong>\ndf.index\n\nDescribe DataFrame columns\n<\/strong>\ndf.columns\n\nInfo on DataFrame\n<\/strong>\ndf.info()\n\nNumber of non-NA values\n<\/strong>\ndf.count()\n\nSummary\n<\/strong>Sum of values\n<\/strong><\/em>\ndf.sum()\n\nCumulative sum of values\n<\/strong><\/em>\ndf.cumsum()\n\nMinimum\/maximum values\n<\/strong><\/em>\ndf.min()\/df.max()\n\nMinimum\/Maximum index value\n<\/em><\/strong>\ndf.idxmin()\/df.idxmax() \n\nSummary statistics\n<\/strong>\ndf.describe()\n\nMean of values\n<\/strong><\/em>\ndf.mean()\n\nMedian of values\n<\/em><\/strong>\ndf.median()\n\nApplying Functions\n<\/strong>f = lambda x: x*2\n\nApply function\n<\/em><\/strong>\ndf.apply(f)\n\nApply function element-wise\n<\/em><\/strong>\ndf.applynap(f) \n\nInternal Data Alignment\n<\/strong>NA values are introduced in the indices that don't overlap:\n<\/em>\ns3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])\ns + s3\na 10.0\nb NaN\nc 5.0\nd 7.0\n\nArithmetic Operations with Fill Methods\n<\/strong>You can also do the internal data alignment yourself with the help of the fill methods:<\/em>\n\ns.add(s3, fill_value=0)\na 10.0\nb -5.0\nc 5.0\nd 7.0\ns.sub(s3, fill_value=2)\ns.div(s3, fill_value=4)\ns.mul(s3, fill_value=3)\n\n\n\n<\/strong>https:\/\/pandas.pydata.org\/Pandas_Cheat_Sheet.pdf<\/a>\n\n\nSOURCE: DATACAMP, PANDAS.PYDATA.ORG\r\n<\/em><\/strong>-----------------------------------------------------------------------\nimmadshahid.com<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"Here are the functions in the pandas’ module or library:<\/p>\n","protected":false},"author":1,"featured_media":430,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[130,124,128,120,92,54,129,127],"tags":[132,131,126],"class_list":["post-429","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cheat-sheet","category-data-analytics","category-data-science","category-data-sscience","category-earning-and-learning","category-education","category-pandas","category-python","tag-data-science","tag-pandas","tag-python"],"_links":{"self":[{"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/posts\/429","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/comments?post=429"}],"version-history":[{"count":1,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/posts\/429\/revisions"}],"predecessor-version":[{"id":431,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/posts\/429\/revisions\/431"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/media\/430"}],"wp:attachment":[{"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/media?parent=429"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/categories?post=429"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/immadshahid.com\/blog\/wp-json\/wp\/v2\/tags?post=429"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}