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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 1,
  6. "metadata": {
  7. "collapsed": true
  8. },
  9. "outputs": [],
  10. "source": [
  11. "import pandas as pd"
  12. ]
  13. },
  14. {
  15. "cell_type": "code",
  16. "execution_count": 2,
  17. "metadata": {
  18. "collapsed": true
  19. },
  20. "outputs": [],
  21. "source": [
  22. "df1 = pd.DataFrame({'IPv4 Address': ['69.89.31.226', '61.89.31.226', '79.81.31.226', '69.81.31.225'],\n",
  23. " 'some_column': [1,2,3,4], 'abother_column': ['q', 'f', 'g', 'h']})\n",
  24. "df2 = pd.DataFrame({'IPv4 Address': ['69.89.31.226', '61.89.37.226', '79.81.31.226', '69.81.31.236', '69.81.31.222'],\n",
  25. " 'some_column1': [11,21,31,41, 51], 'abother_column1': ['qq', 'f1', 'g1', 'h1', 'j1']})"
  26. ]
  27. },
  28. {
  29. "cell_type": "code",
  30. "execution_count": 3,
  31. "metadata": {},
  32. "outputs": [
  33. {
  34. "data": {
  35. "text/html": [
  36. "<div>\n",
  37. "<style>\n",
  38. " .dataframe thead tr:only-child th {\n",
  39. " text-align: right;\n",
  40. " }\n",
  41. "\n",
  42. " .dataframe thead th {\n",
  43. " text-align: left;\n",
  44. " }\n",
  45. "\n",
  46. " .dataframe tbody tr th {\n",
  47. " vertical-align: top;\n",
  48. " }\n",
  49. "</style>\n",
  50. "<table border=\"1\" class=\"dataframe\">\n",
  51. " <thead>\n",
  52. " <tr style=\"text-align: right;\">\n",
  53. " <th></th>\n",
  54. " <th>IPv4 Address</th>\n",
  55. " <th>abother_column</th>\n",
  56. " <th>some_column</th>\n",
  57. " </tr>\n",
  58. " </thead>\n",
  59. " <tbody>\n",
  60. " <tr>\n",
  61. " <th>0</th>\n",
  62. " <td>69.89.31.226</td>\n",
  63. " <td>q</td>\n",
  64. " <td>1</td>\n",
  65. " </tr>\n",
  66. " <tr>\n",
  67. " <th>1</th>\n",
  68. " <td>61.89.31.226</td>\n",
  69. " <td>f</td>\n",
  70. " <td>2</td>\n",
  71. " </tr>\n",
  72. " <tr>\n",
  73. " <th>2</th>\n",
  74. " <td>79.81.31.226</td>\n",
  75. " <td>g</td>\n",
  76. " <td>3</td>\n",
  77. " </tr>\n",
  78. " <tr>\n",
  79. " <th>3</th>\n",
  80. " <td>69.81.31.225</td>\n",
  81. " <td>h</td>\n",
  82. " <td>4</td>\n",
  83. " </tr>\n",
  84. " </tbody>\n",
  85. "</table>\n",
  86. "</div>"
  87. ],
  88. "text/plain": [
  89. " IPv4 Address abother_column some_column\n",
  90. "0 69.89.31.226 q 1\n",
  91. "1 61.89.31.226 f 2\n",
  92. "2 79.81.31.226 g 3\n",
  93. "3 69.81.31.225 h 4"
  94. ]
  95. },
  96. "execution_count": 3,
  97. "metadata": {},
  98. "output_type": "execute_result"
  99. }
  100. ],
  101. "source": [
  102. "df1"
  103. ]
  104. },
  105. {
  106. "cell_type": "code",
  107. "execution_count": 4,
  108. "metadata": {},
  109. "outputs": [
  110. {
  111. "data": {
  112. "text/html": [
  113. "<div>\n",
  114. "<style>\n",
  115. " .dataframe thead tr:only-child th {\n",
  116. " text-align: right;\n",
  117. " }\n",
  118. "\n",
  119. " .dataframe thead th {\n",
  120. " text-align: left;\n",
  121. " }\n",
  122. "\n",
  123. " .dataframe tbody tr th {\n",
  124. " vertical-align: top;\n",
  125. " }\n",
  126. "</style>\n",
  127. "<table border=\"1\" class=\"dataframe\">\n",
  128. " <thead>\n",
  129. " <tr style=\"text-align: right;\">\n",
  130. " <th></th>\n",
  131. " <th>IPv4 Address</th>\n",
  132. " <th>abother_column1</th>\n",
  133. " <th>some_column1</th>\n",
  134. " </tr>\n",
  135. " </thead>\n",
  136. " <tbody>\n",
  137. " <tr>\n",
  138. " <th>0</th>\n",
  139. " <td>69.89.31.226</td>\n",
  140. " <td>qq</td>\n",
  141. " <td>11</td>\n",
  142. " </tr>\n",
  143. " <tr>\n",
  144. " <th>1</th>\n",
  145. " <td>61.89.37.226</td>\n",
  146. " <td>f1</td>\n",
  147. " <td>21</td>\n",
  148. " </tr>\n",
  149. " <tr>\n",
  150. " <th>2</th>\n",
  151. " <td>79.81.31.226</td>\n",
  152. " <td>g1</td>\n",
  153. " <td>31</td>\n",
  154. " </tr>\n",
  155. " <tr>\n",
  156. " <th>3</th>\n",
  157. " <td>69.81.31.236</td>\n",
  158. " <td>h1</td>\n",
  159. " <td>41</td>\n",
  160. " </tr>\n",
  161. " <tr>\n",
  162. " <th>4</th>\n",
  163. " <td>69.81.31.222</td>\n",
  164. " <td>j1</td>\n",
  165. " <td>51</td>\n",
  166. " </tr>\n",
  167. " </tbody>\n",
  168. "</table>\n",
  169. "</div>"
  170. ],
  171. "text/plain": [
  172. " IPv4 Address abother_column1 some_column1\n",
  173. "0 69.89.31.226 qq 11\n",
  174. "1 61.89.37.226 f1 21\n",
  175. "2 79.81.31.226 g1 31\n",
  176. "3 69.81.31.236 h1 41\n",
  177. "4 69.81.31.222 j1 51"
  178. ]
  179. },
  180. "execution_count": 4,
  181. "metadata": {},
  182. "output_type": "execute_result"
  183. }
  184. ],
  185. "source": [
  186. "df2"
  187. ]
  188. },
  189. {
  190. "cell_type": "code",
  191. "execution_count": 5,
  192. "metadata": {},
  193. "outputs": [
  194. {
  195. "data": {
  196. "text/html": [
  197. "<div>\n",
  198. "<style>\n",
  199. " .dataframe thead tr:only-child th {\n",
  200. " text-align: right;\n",
  201. " }\n",
  202. "\n",
  203. " .dataframe thead th {\n",
  204. " text-align: left;\n",
  205. " }\n",
  206. "\n",
  207. " .dataframe tbody tr th {\n",
  208. " vertical-align: top;\n",
  209. " }\n",
  210. "</style>\n",
  211. "<table border=\"1\" class=\"dataframe\">\n",
  212. " <thead>\n",
  213. " <tr style=\"text-align: right;\">\n",
  214. " <th></th>\n",
  215. " <th>IPv4 Address</th>\n",
  216. " <th>abother_column</th>\n",
  217. " <th>some_column</th>\n",
  218. " <th>abother_column1</th>\n",
  219. " <th>some_column1</th>\n",
  220. " </tr>\n",
  221. " </thead>\n",
  222. " <tbody>\n",
  223. " <tr>\n",
  224. " <th>0</th>\n",
  225. " <td>69.89.31.226</td>\n",
  226. " <td>q</td>\n",
  227. " <td>1</td>\n",
  228. " <td>qq</td>\n",
  229. " <td>11</td>\n",
  230. " </tr>\n",
  231. " <tr>\n",
  232. " <th>1</th>\n",
  233. " <td>79.81.31.226</td>\n",
  234. " <td>g</td>\n",
  235. " <td>3</td>\n",
  236. " <td>g1</td>\n",
  237. " <td>31</td>\n",
  238. " </tr>\n",
  239. " </tbody>\n",
  240. "</table>\n",
  241. "</div>"
  242. ],
  243. "text/plain": [
  244. " IPv4 Address abother_column some_column abother_column1 some_column1\n",
  245. "0 69.89.31.226 q 1 qq 11\n",
  246. "1 79.81.31.226 g 3 g1 31"
  247. ]
  248. },
  249. "execution_count": 5,
  250. "metadata": {},
  251. "output_type": "execute_result"
  252. }
  253. ],
  254. "source": [
  255. "pd.merge(df1, df2, on='IPv4 Address', how='inner')"
  256. ]
  257. },
  258. {
  259. "cell_type": "code",
  260. "execution_count": null,
  261. "metadata": {
  262. "collapsed": true
  263. },
  264. "outputs": [],
  265. "source": []
  266. }
  267. ],
  268. "metadata": {
  269. "kernelspec": {
  270. "display_name": "Python 3",
  271. "language": "python",
  272. "name": "python3"
  273. },
  274. "language_info": {
  275. "codemirror_mode": {
  276. "name": "ipython",
  277. "version": 3
  278. },
  279. "file_extension": ".py",
  280. "mimetype": "text/x-python",
  281. "name": "python",
  282. "nbconvert_exporter": "python",
  283. "pygments_lexer": "ipython3",
  284. "version": "3.5.3"
  285. }
  286. },
  287. "nbformat": 4,
  288. "nbformat_minor": 2
  289. }
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