pd_np_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "58c3ac81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81ae0a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([1,3,5,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9cce204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5650c4fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat1 = plt.plot(arr1, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10d6f34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "store = {\n",
    "    'product' : ['a', 'b', 'c', 'd', 'e', 'f'],\n",
    "    'price' : [100, 115, 95, 98, 105, 110]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d52a710b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>product</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product  price\n",
       "0       a    100\n",
       "1       b    115\n",
       "2       c     95\n",
       "3       d     98\n",
       "4       e    105\n",
       "5       f    110"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1 = pd.DataFrame(store)\n",
    "store1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a216ebfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat2 = plt.plot(store['product'], store['price'], '*', ms = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "337129d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('D:\\\\vishal data\\\\csv file\\\\covid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9eaefc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>No</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>active</th>\n",
       "      <th>passive</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dose1</th>\n",
       "      <th>dose2</th>\n",
       "      <th>dose3</th>\n",
       "      <th>precaution_dose</th>\n",
       "      <th>total_doses</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Andaman and Nicobar</td>\n",
       "      <td>10742</td>\n",
       "      <td>74</td>\n",
       "      <td>10612</td>\n",
       "      <td>67</td>\n",
       "      <td>313284</td>\n",
       "      <td>320383</td>\n",
       "      <td>236936</td>\n",
       "      <td>53427</td>\n",
       "      <td>991263</td>\n",
       "      <td>426251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No                state  confirmed  active  passive  deaths   dose1  \\\n",
       "0   1  Andaman and Nicobar      10742      74    10612      67  313284   \n",
       "\n",
       "    dose2   dose3  precaution_dose  total_doses  population  \n",
       "0  320383  236936            53427       991263      426251  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "927f969c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '_AXIS_LEN',\n",
       " '_AXIS_ORDERS',\n",
       " '_AXIS_TO_AXIS_NUMBER',\n",
       " '_HANDLED_TYPES',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__annotations__',\n",
       " '__array__',\n",
       " '__array_priority__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__finalize__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattr__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__module__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
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       " '_hidden_attrs',\n",
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       " '_info_axis',\n",
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       " '_info_axis_number',\n",
       " '_info_repr',\n",
       " '_init_mgr',\n",
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       " '_internal_names',\n",
       " '_internal_names_set',\n",
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       " '_is_homogeneous_type',\n",
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       " '_is_mixed_type',\n",
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       " '_iset_item',\n",
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       " '_iter_column_arrays',\n",
       " '_ixs',\n",
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       " '_logical_method',\n",
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       " '_maybe_update_cacher',\n",
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       " '_reduce',\n",
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       " '_repr_fits_vertical_',\n",
       " '_repr_html_',\n",
       " '_repr_latex_',\n",
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       " '_update_inplace',\n",
       " '_validate_dtype',\n",
       " '_values',\n",
       " '_where',\n",
       " 'abs',\n",
       " 'add',\n",
       " 'add_prefix',\n",
       " 'add_suffix',\n",
       " 'agg',\n",
       " 'aggregate',\n",
       " 'align',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply',\n",
       " 'applymap',\n",
       " 'asfreq',\n",
       " 'asof',\n",
       " 'assign',\n",
       " 'astype',\n",
       " 'at',\n",
       " 'at_time',\n",
       " 'attrs',\n",
       " 'axes',\n",
       " 'backfill',\n",
       " 'between_time',\n",
       " 'bfill',\n",
       " 'bool',\n",
       " 'boxplot',\n",
       " 'clip',\n",
       " 'columns',\n",
       " 'combine',\n",
       " 'combine_first',\n",
       " 'compare',\n",
       " 'convert_dtypes',\n",
       " 'copy',\n",
       " 'corr',\n",
       " 'corrwith',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'describe',\n",
       " 'diff',\n",
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       " 'divide',\n",
       " 'dot',\n",
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       " 'eq',\n",
       " 'equals',\n",
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       " 'ewm',\n",
       " 'expanding',\n",
       " 'explode',\n",
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       " 'first',\n",
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       " 'flags',\n",
       " 'floordiv',\n",
       " 'from_dict',\n",
       " 'from_records',\n",
       " 'ge',\n",
       " 'get',\n",
       " 'groupby',\n",
       " 'gt',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'iat',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'iloc',\n",
       " 'index',\n",
       " 'infer_objects',\n",
       " 'info',\n",
       " 'insert',\n",
       " 'interpolate',\n",
       " 'isin',\n",
       " 'isna',\n",
       " 'isnull',\n",
       " 'items',\n",
       " 'iteritems',\n",
       " 'iterrows',\n",
       " 'itertuples',\n",
       " 'join',\n",
       " 'keys',\n",
       " 'kurt',\n",
       " 'kurtosis',\n",
       " 'last',\n",
       " 'last_valid_index',\n",
       " 'le',\n",
       " 'loc',\n",
       " 'lookup',\n",
       " 'lt',\n",
       " 'mad',\n",
       " 'mask',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'melt',\n",
       " 'memory_usage',\n",
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       " 'notna',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'pivot',\n",
       " 'pivot_table',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'price',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'quantile',\n",
       " 'query',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'rdiv',\n",
       " 'reindex',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'replace',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'select_dtypes',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'set_flags',\n",
       " 'set_index',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'style',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_feather',\n",
       " 'to_gbq',\n",
       " 'to_hdf',\n",
       " 'to_html',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_parquet',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_records',\n",
       " 'to_sql',\n",
       " 'to_stata',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'to_xml',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(store1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "687be205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product      f\n",
       "price      115\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52829dae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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