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jaiswalakshay508-maker/string function.ipynb

Created May 3, 2026 13:03
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string function.ipynb
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-12-14T10:26:10.076853Z",
"start_time": "2025-12-14T10:26:02.169768Z"
}
},
"source": "import pandas as pd",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-05T12:57:25.372074Z",
"start_time": "2025-12-05T12:57:25.281064Z"
}
},
"cell_type": "code",
"source": [
"df=pd.read_csv('expense.csv')\n",
"df"
],
"id": "dbe5c9ccbddf7bdc",
"outputs": [
{
"data": {
"text/plain": [
" date amount category\n",
"0 03/01/2024 520 Groceries\n",
"1 07/01/2024 150 Transport\n",
"2 15/01/2024 980 Electronics\n",
"3 28/01/2024 220 Dining\n",
"4 05/02/2024 430 Rent\n",
"5 11/02/2024 85 Groceries\n",
"6 19/02/2024 60 Snacks\n",
"7 03/03/2024 255 Medical\n",
"8 18/03/2024 145 Transport\n",
"9 25/03/2024 600 Groceries\n",
"10 04/04/2024 210 Utilities\n",
"11 22/04/2024 140 Snacks\n",
"12 01/05/2024 700 Rent\n",
"13 13/05/2024 475 Groceries\n",
"14 29/05/2024 310 Dining\n",
"15 08/06/2024 260 Transport\n",
"16 21/06/2024 120 Snacks\n",
"17 30/06/2024 150 Entertainment\n",
"18 07/07/2024 510 Groceries\n",
"19 19/07/2024 210 Utilities\n",
"20 04/08/2024 130 Transport\n",
"21 26/08/2024 880 Electronics\n",
"22 06/09/2024 900 Rent\n",
"23 14/09/2024 345 Groceries\n",
"24 25/09/2024 90 Snacks\n",
"25 03/10/2024 240 Medical\n",
"26 17/10/2024 190 Transport\n",
"27 29/10/2024 160 Dining\n",
"28 05/11/2024 520 Groceries\n",
"29 22/11/2024 210 Utilities\n",
"30 08/12/2024 150 Snacks\n",
"31 20/12/2024 105 Transport\n",
"32 04/01/2025 540 Groceries\n",
"33 15/01/2025 985 Electronics\n",
"34 28/01/2025 260 Dining\n",
"35 10/02/2025 900 Rent\n",
"36 19/02/2025 180 Transport\n",
"37 27/02/2025 115 Snacks\n",
"38 09/03/2025 620 Groceries\n",
"39 22/03/2025 145 Transport\n",
"40 02/04/2025 210 Utilities\n",
"41 26/04/2025 345 Medical\n",
"42 03/05/2025 705 Rent\n",
"43 19/05/2025 320 Groceries\n",
"44 01/06/2025 155 Snacks\n",
"45 18/06/2025 245 Transport\n",
"46 05/07/2025 520 Groceries\n",
"47 23/07/2025 210 Utilities\n",
"48 09/08/2025 130 Entertainment\n",
"49 27/08/2025 890 Electronics\n",
"50 07/09/2025 950 Rent\n",
"51 18/09/2025 310 Groceries\n",
"52 29/09/2025 95 Snacks\n",
"53 04/10/2025 235 Medical\n",
"54 21/10/2025 190 Transport\n",
"55 01/11/2025 540 Groceries\n",
"56 19/11/2025 210 Dining\n",
"57 08/12/2025 160 Snacks\n",
"58 22/12/2025 110 Transport"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>03/01/2024</td>\n",
" <td>520</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>07/01/2024</td>\n",
" <td>150</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>15/01/2024</td>\n",
" <td>980</td>\n",
" <td>Electronics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>28/01/2024</td>\n",
" <td>220</td>\n",
" <td>Dining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>05/02/2024</td>\n",
" <td>430</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11/02/2024</td>\n",
" <td>85</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>19/02/2024</td>\n",
" <td>60</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>03/03/2024</td>\n",
" <td>255</td>\n",
" <td>Medical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>18/03/2024</td>\n",
" <td>145</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>25/03/2024</td>\n",
" <td>600</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>04/04/2024</td>\n",
" <td>210</td>\n",
" <td>Utilities</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>22/04/2024</td>\n",
" <td>140</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>01/05/2024</td>\n",
" <td>700</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13/05/2024</td>\n",
" <td>475</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>29/05/2024</td>\n",
" <td>310</td>\n",
" <td>Dining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>08/06/2024</td>\n",
" <td>260</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>21/06/2024</td>\n",
" <td>120</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>30/06/2024</td>\n",
" <td>150</td>\n",
" <td>Entertainment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>07/07/2024</td>\n",
" <td>510</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19/07/2024</td>\n",
" <td>210</td>\n",
" <td>Utilities</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>04/08/2024</td>\n",
" <td>130</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>26/08/2024</td>\n",
" <td>880</td>\n",
" <td>Electronics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>06/09/2024</td>\n",
" <td>900</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>14/09/2024</td>\n",
" <td>345</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25/09/2024</td>\n",
" <td>90</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>03/10/2024</td>\n",
" <td>240</td>\n",
" <td>Medical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>17/10/2024</td>\n",
" <td>190</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>29/10/2024</td>\n",
" <td>160</td>\n",
" <td>Dining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>05/11/2024</td>\n",
" <td>520</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>22/11/2024</td>\n",
" <td>210</td>\n",
" <td>Utilities</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>08/12/2024</td>\n",
" <td>150</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>20/12/2024</td>\n",
" <td>105</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>04/01/2025</td>\n",
" <td>540</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>15/01/2025</td>\n",
" <td>985</td>\n",
" <td>Electronics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>28/01/2025</td>\n",
" <td>260</td>\n",
" <td>Dining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>10/02/2025</td>\n",
" <td>900</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>19/02/2025</td>\n",
" <td>180</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>27/02/2025</td>\n",
" <td>115</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>09/03/2025</td>\n",
" <td>620</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>22/03/2025</td>\n",
" <td>145</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>02/04/2025</td>\n",
" <td>210</td>\n",
" <td>Utilities</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>26/04/2025</td>\n",
" <td>345</td>\n",
" <td>Medical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>03/05/2025</td>\n",
" <td>705</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>19/05/2025</td>\n",
" <td>320</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>01/06/2025</td>\n",
" <td>155</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>18/06/2025</td>\n",
" <td>245</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>05/07/2025</td>\n",
" <td>520</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>23/07/2025</td>\n",
" <td>210</td>\n",
" <td>Utilities</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>09/08/2025</td>\n",
" <td>130</td>\n",
" <td>Entertainment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>27/08/2025</td>\n",
" <td>890</td>\n",
" <td>Electronics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>07/09/2025</td>\n",
" <td>950</td>\n",
" <td>Rent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>18/09/2025</td>\n",
" <td>310</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>29/09/2025</td>\n",
" <td>95</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>04/10/2025</td>\n",
" <td>235</td>\n",
" <td>Medical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>21/10/2025</td>\n",
" <td>190</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>01/11/2025</td>\n",
" <td>540</td>\n",
" <td>Groceries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>19/11/2025</td>\n",
" <td>210</td>\n",
" <td>Dining</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>08/12/2025</td>\n",
" <td>160</td>\n",
" <td>Snacks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>22/12/2025</td>\n",
" <td>110</td>\n",
" <td>Transport</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-05T12:59:43.180972Z",
"start_time": "2025-12-05T12:59:42.901985Z"
}
},
"cell_type": "code",
"source": [
"df['date']=pd.to_datetime(df['date'],format='%d/%m/$Y')\n",
"df.head()"
],
"id": "72ff88ed88ec2845",
"outputs": [
{
"ename": "ValueError",
"evalue": "time data \"03/01/2024\" doesn't match format \"%d/%m/$Y\", at position 0. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[5]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33mdate\u001B[39m\u001B[33m'\u001B[39m]=\u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mto_datetime\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mdate\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[38;5;28;43mformat\u001B[39;49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[38;5;132;43;01m%d\u001B[39;49;00m\u001B[33;43m/\u001B[39;49m\u001B[33;43m%\u001B[39;49m\u001B[33;43mm/$Y\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 2\u001B[39m df.head()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1072\u001B[39m, in \u001B[36mto_datetime\u001B[39m\u001B[34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001B[39m\n\u001B[32m 1070\u001B[39m result = arg.map(cache_array)\n\u001B[32m 1071\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1072\u001B[39m values = \u001B[43mconvert_listlike\u001B[49m\u001B[43m(\u001B[49m\u001B[43marg\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_values\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mformat\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 1073\u001B[39m result = arg._constructor(values, index=arg.index, name=arg.name)\n\u001B[32m 1074\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arg, (ABCDataFrame, abc.MutableMapping)):\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:435\u001B[39m, in \u001B[36m_convert_listlike_datetimes\u001B[39m\u001B[34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001B[39m\n\u001B[32m 433\u001B[39m \u001B[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001B[39;00m\n\u001B[32m 434\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mformat\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mformat\u001B[39m != \u001B[33m\"\u001B[39m\u001B[33mmixed\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m435\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_array_strptime_with_fallback\u001B[49m\u001B[43m(\u001B[49m\u001B[43marg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mutc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mformat\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexact\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 437\u001B[39m result, tz_parsed = objects_to_datetime64(\n\u001B[32m 438\u001B[39m arg,\n\u001B[32m 439\u001B[39m dayfirst=dayfirst,\n\u001B[32m (...)\u001B[39m\u001B[32m 443\u001B[39m allow_object=\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[32m 444\u001B[39m )\n\u001B[32m 446\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m tz_parsed \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 447\u001B[39m \u001B[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001B[39;00m\n\u001B[32m 448\u001B[39m \u001B[38;5;66;03m# is in UTC\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:469\u001B[39m, in \u001B[36m_array_strptime_with_fallback\u001B[39m\u001B[34m(arg, name, utc, fmt, exact, errors)\u001B[39m\n\u001B[32m 458\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_array_strptime_with_fallback\u001B[39m(\n\u001B[32m 459\u001B[39m arg,\n\u001B[32m 460\u001B[39m name,\n\u001B[32m (...)\u001B[39m\u001B[32m 464\u001B[39m errors: \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m 465\u001B[39m ) -> Index:\n\u001B[32m 466\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 467\u001B[39m \u001B[33;03m Call array_strptime, with fallback behavior depending on 'errors'.\u001B[39;00m\n\u001B[32m 468\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m469\u001B[39m result, tz_out = \u001B[43marray_strptime\u001B[49m\u001B[43m(\u001B[49m\u001B[43marg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexact\u001B[49m\u001B[43m=\u001B[49m\u001B[43mexact\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m=\u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mutc\u001B[49m\u001B[43m=\u001B[49m\u001B[43mutc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 470\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m tz_out \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 471\u001B[39m unit = np.datetime_data(result.dtype)[\u001B[32m0\u001B[39m]\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/tslibs/strptime.pyx:501\u001B[39m, in \u001B[36mpandas._libs.tslibs.strptime.array_strptime\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/tslibs/strptime.pyx:451\u001B[39m, in \u001B[36mpandas._libs.tslibs.strptime.array_strptime\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/tslibs/strptime.pyx:583\u001B[39m, in \u001B[36mpandas._libs.tslibs.strptime._parse_with_format\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[31mValueError\u001B[39m: time data \"03/01/2024\" doesn't match format \"%d/%m/$Y\", at position 0. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
]
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-05T13:04:10.590074Z",
"start_time": "2025-12-05T13:04:09.567992Z"
}
},
"cell_type": "code",
"source": [
"df['month']=df['date'].dt.month\n",
"df['year']=df['date'].dt.year\n",
"df['day']=df['date'].dt.day\n",
"df['day name']=df['date'].dt.day_name()\n",
"df['month_name']=df['date'].dt.month_name()\n",
"df"
],
"id": "8944883b5cf047a8",
"outputs": [
{
"ename": "AttributeError",
"evalue": "Can only use .dt accessor with datetimelike values",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[6]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33mmonth\u001B[39m\u001B[33m'\u001B[39m]=\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mdate\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdt\u001B[49m.month\n\u001B[32m 2\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33myear\u001B[39m\u001B[33m'\u001B[39m]=df[\u001B[33m'\u001B[39m\u001B[33mdate\u001B[39m\u001B[33m'\u001B[39m].dt.year\n\u001B[32m 3\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33mday\u001B[39m\u001B[33m'\u001B[39m]=df[\u001B[33m'\u001B[39m\u001B[33mdate\u001B[39m\u001B[33m'\u001B[39m].dt.day\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:6321\u001B[39m, in \u001B[36mNDFrame.__getattr__\u001B[39m\u001B[34m(self, name)\u001B[39m\n\u001B[32m 6314\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m 6315\u001B[39m name \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._internal_names_set\n\u001B[32m 6316\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m name \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._metadata\n\u001B[32m 6317\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m name \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._accessors\n\u001B[32m 6318\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m._info_axis._can_hold_identifiers_and_holds_name(name)\n\u001B[32m 6319\u001B[39m ):\n\u001B[32m 6320\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m[name]\n\u001B[32m-> \u001B[39m\u001B[32m6321\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mobject\u001B[39;49m\u001B[43m.\u001B[49m\u001B[34;43m__getattribute__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\accessor.py:224\u001B[39m, in \u001B[36mCachedAccessor.__get__\u001B[39m\u001B[34m(self, obj, cls)\u001B[39m\n\u001B[32m 221\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m obj \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 222\u001B[39m \u001B[38;5;66;03m# we're accessing the attribute of the class, i.e., Dataset.geo\u001B[39;00m\n\u001B[32m 223\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._accessor\n\u001B[32m--> \u001B[39m\u001B[32m224\u001B[39m accessor_obj = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_accessor\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 225\u001B[39m \u001B[38;5;66;03m# Replace the property with the accessor object. Inspired by:\u001B[39;00m\n\u001B[32m 226\u001B[39m \u001B[38;5;66;03m# https://www.pydanny.com/cached-property.html\u001B[39;00m\n\u001B[32m 227\u001B[39m \u001B[38;5;66;03m# We need to use object.__setattr__ because we overwrite __setattr__ on\u001B[39;00m\n\u001B[32m 228\u001B[39m \u001B[38;5;66;03m# NDFrame\u001B[39;00m\n\u001B[32m 229\u001B[39m \u001B[38;5;28mobject\u001B[39m.\u001B[34m__setattr__\u001B[39m(obj, \u001B[38;5;28mself\u001B[39m._name, accessor_obj)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\accessors.py:643\u001B[39m, in \u001B[36mCombinedDatetimelikeProperties.__new__\u001B[39m\u001B[34m(cls, data)\u001B[39m\n\u001B[32m 640\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(data.dtype, PeriodDtype):\n\u001B[32m 641\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m PeriodProperties(data, orig)\n\u001B[32m--> \u001B[39m\u001B[32m643\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mCan only use .dt accessor with datetimelike values\u001B[39m\u001B[33m\"\u001B[39m)\n",
"\u001B[31mAttributeError\u001B[39m: Can only use .dt accessor with datetimelike values"
]
}
],
"execution_count": 6
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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