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‎pandas.ipynb

Lines changed: 276 additions & 2 deletions
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyP45TVE/kj6jRubvwxR8kn1",
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"authorship_tag": "ABX9TyO4N8M+ba81EU5bXKbQdO/W",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -67,9 +67,143 @@
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"metadata": {
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"id": "5jgqcBUNkPjV"
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},
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"execution_count": null,
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"# Create a DataFrame\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"# Display the first few rows of the DataFrame\n",
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"print(\"Initial DataFrame:\")\n",
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"print(df.head(), \"\\n\")\n",
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"\n",
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"# 1. Create a new column 'B_D_Ratio' as the ratio of column B to D\n",
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"df['B_D_Ratio'] = df['B'] / df['D']\n",
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"print(\"DataFrame with 'B_D_Ratio':\")\n",
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"print(df[['A', 'B', 'D', 'B_D_Ratio']].head(), \"\\n\")\n",
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"\n",
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"# 2. Filter rows where column B is greater than the median value of B\n",
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"median_B = df['B'].median()\n",
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"df_filtered = df[df['B'] > median_B]\n",
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"print(f\"Filtered DataFrame (B > {median_B}):\")\n",
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"print(df_filtered[['A', 'B']].reset_index(drop=True), \"\\n\")\n",
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"\n",
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"# 3. Create a new column 'Country_initial' with the first letter of the country name (column A)\n",
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"df['Country_initial'] = df['A'].str[0]\n",
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"print(\"DataFrame with 'Country_initial':\")\n",
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"print(df[['A', 'Country_initial']].head(), \"\\n\")\n",
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"\n",
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"# 4. Group by the 'Country_initial' and compute the mean of columns B and D\n",
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"grouped = df.groupby('Country_initial')[['B', 'D']].mean().reset_index()\n",
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"print(\"Grouped DataFrame (mean of B and D by Country_initial):\")\n",
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"print(grouped, \"\\n\")\n",
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"\n",
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"# 5. Sort the DataFrame by column C in descending order\n",
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"df_sorted = df.sort_values(by='C', ascending=False)\n",
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"print(\"DataFrame sorted by column C (descending):\")\n",
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"print(df_sorted[['A', 'C']].head())"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "CJe0PUuRSKge",
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"outputId": "694a12b8-1d98-4423-9cd1-cd4f2ef440ed"
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},
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Initial DataFrame:\n",
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" A B C D E F\n",
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"0 Argentina 960.94 11.23 242619 78.88 45.38\n",
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"1 Australia 42.39 6.44 1971859 77.25 75.87\n",
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"2 Austria 416.17 8.16 9058867 77.82 69.73\n",
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"3 Belgium 338.70 28.11 4526583 69.97 40.81\n",
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"4 Brazil 1106.02 19.51 5757197 76.79 45.13 \n",
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"\n",
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"DataFrame with 'B_D_Ratio':\n",
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" A B D B_D_Ratio\n",
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"0 Argentina 960.94 242619 0.003961\n",
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"1 Australia 42.39 1971859 0.000021\n",
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"2 Austria 416.17 9058867 0.000046\n",
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"3 Belgium 338.70 4526583 0.000075\n",
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"4 Brazil 1106.02 5757197 0.000192 \n",
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"\n",
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"Filtered DataFrame (B > 698.14):\n",
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" A B\n",
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"0 Argentina 960.94\n",
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"1 Brazil 1106.02\n",
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"2 Canada 1016.67\n",
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"3 China 1338.81\n",
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"4 France 760.51\n",
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"5 India 976.58\n",
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"6 Indonesia 819.69\n",
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"7 Italy 885.95\n",
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"8 Japan 1215.10\n",
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"9 Mexico 1209.70\n",
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"10 Netherlands 1048.72\n",
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"11 Pakistan 1436.03\n",
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"12 Spain 1272.00\n",
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"13 United States 907.57\n",
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"14 Bangladesh 1211.66\n",
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"15 Vietnam 1095.95\n",
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"16 Turkey 806.66\n",
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"17 South Africa 1459.81\n",
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"18 Ukraine 830.30\n",
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"19 Poland 1244.96\n",
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"20 Saudi Arabia 929.69\n",
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"21 Argentina 1293.25\n",
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"22 Sweden 868.14\n",
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"23 Switzerland 1058.33\n",
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"24 Peru 955.35 \n",
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"\n",
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"DataFrame with 'Country_initial':\n",
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" A Country_initial\n",
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"0 Argentina A\n",
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"1 Australia A\n",
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"2 Austria A\n",
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"3 Belgium B\n",
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"4 Brazil B \n",
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"\n",
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"Grouped DataFrame (mean of B and D by Country_initial):\n",
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" Country_initial B D\n",
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"0 A 678.187500 3.121134e+06\n",
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"1 B 682.472500 4.424265e+06\n",
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"2 C 727.755000 5.347219e+06\n",
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"3 D 635.770000 2.703446e+06\n",
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"4 E 49.550000 7.662220e+06\n",
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"5 F 546.185000 2.286690e+06\n",
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"6 G 173.465000 5.747861e+06\n",
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"7 H 156.000000 8.902897e+06\n",
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"8 I 754.195000 6.006751e+06\n",
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"9 J 1215.100000 3.387174e+06\n",
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"10 M 449.570000 3.919273e+06\n",
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"11 N 587.562500 7.476341e+06\n",
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"12 P 1036.137500 2.659610e+06\n",
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"13 R 143.660000 8.247443e+06\n",
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"14 S 870.082857 4.320352e+06\n",
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"15 T 605.070000 6.621555e+06\n",
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"16 U 694.526667 5.178348e+06\n",
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"17 V 1095.950000 4.067343e+06 \n",
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"\n",
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"DataFrame sorted by column C (descending):\n",
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" A C\n",
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"36 Poland 29.93\n",
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"46 Hong Kong 29.88\n",
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"10 Finland 29.69\n",
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"48 Peru 29.14\n",
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"21 Netherlands 28.30\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
@@ -1344,6 +1478,146 @@
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "7sb6BOq9NYAq"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import requests\n",
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"\n",
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"def fetch_pokemon_names():\n",
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" \"\"\"Generator function to yield Pokémon names from the API\"\"\"\n",
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" url = \"https://pokeapi.co/api/v2/pokemon?limit=2000\"\n",
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" response = requests.get(url)\n",
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"\n",
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" if response.status_code == 200:\n",
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" data = response.json()\n",
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" for pokemon in data['results']: # Iterate over results\n",
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" yield pokemon['name'] # Yield one name at a time\n",
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" else:\n",
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" yield \"Failed to fetch data from PokeAPI\"\n",
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"\n",
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"# Using the generator\n",
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"pokemon_generator = fetch_pokemon_names()\n",
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"\n",
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"# Example: Print first 10 Pokémon names\n",
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"print(\"Available Pokémon names:\")\n",
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"for i, name in enumerate(pokemon_generator):\n",
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" if i >= 10: # Stop after 10 names for demonstration\n",
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" break\n",
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" print(name)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "poFjyF2vMHJy",
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"outputId": "de1b1166-c883-4948-f2c5-20b0d2c89848"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Available Pokémon names:\n",
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"bulbasaur\n",
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"ivysaur\n",
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"venusaur\n",
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"charmander\n",
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"charmeleon\n",
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"charizard\n",
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"squirtle\n",
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"wartortle\n",
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"blastoise\n",
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"caterpie\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import requests\n",
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"\n",
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"def fetch_pokemon_batches(batch_size=10):\n",
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" \"\"\"Generator function to yield Pokémon names in batches\"\"\"\n",
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" url = \"https://pokeapi.co/api/v2/pokemon?limit=2000\"\n",
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" response = requests.get(url)\n",
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"\n",
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" if response.status_code == 200:\n",
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" data = response.json()\n",
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" pokemon_list = [pokemon['name'] for pokemon in data['results']]\n",
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"\n",
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" # Yield batches of batch_size\n",
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" for i in range(0, len(pokemon_list), batch_size):\n",
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" yield pokemon_list[i:i + batch_size] # Yield a batch of 10 Pokémon names\n",
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" else:\n",
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" yield [\"Failed to fetch data from PokeAPI\"]\n",
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"\n",
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"# Using the generator to fetch Pokémon in batches of 10\n",
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"pokemon_batch_generator = fetch_pokemon_batches(batch_size=10)\n",
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"\n",
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"# Example: Print the first 5 batches\n",
1570+
"print(\"Available Pokémon names in batches of 10:\")\n",
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"for i, batch in enumerate(pokemon_batch_generator):\n",
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" if i >= 5: # Stop after 5 batches for demonstration\n",
1573+
" break\n",
1574+
" print(f\"Batch {i + 1}: {batch}\")\n"
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],
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"metadata": {
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"id": "0z4s0HsBLv-J",
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"outputId": "8fea6983-4f86-4d9d-d579-8f4dbf12a3f3",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Available Pokémon names in batches of 10:\n",
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"Batch 1: ['bulbasaur', 'ivysaur', 'venusaur', 'charmander', 'charmeleon', 'charizard', 'squirtle', 'wartortle', 'blastoise', 'caterpie']\n",
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"Batch 2: ['metapod', 'butterfree', 'weedle', 'kakuna', 'beedrill', 'pidgey', 'pidgeotto', 'pidgeot', 'rattata', 'raticate']\n",
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"Batch 3: ['spearow', 'fearow', 'ekans', 'arbok', 'pikachu', 'raichu', 'sandshrew', 'sandslash', 'nidoran-f', 'nidorina']\n",
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"Batch 4: ['nidoqueen', 'nidoran-m', 'nidorino', 'nidoking', 'clefairy', 'clefable', 'vulpix', 'ninetales', 'jigglypuff', 'wigglytuff']\n",
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"Batch 5: ['zubat', 'golbat', 'oddish', 'gloom', 'vileplume', 'paras', 'parasect', 'venonat', 'venomoth', 'diglett']\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import requests\n",
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"\n",
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"api_base=a\n",
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"api_key=b\n",
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"client_id=c\n",
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"\n",
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"def get_domain():\n",
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" url=f\"{api_base}/domains/{client_id}?api_key={api_key}\"\n",
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" response = requests.get(url)\n",
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" response.raise_for_status()\n",
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" return response.json()\n",
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"\n",
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"def get_"
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],
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"metadata": {
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"id": "o_WT1MFhHz5M"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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