DOC: Improve reshape\concat

Co-Authored-By: Matěj Štágl <[email protected]>

Author: anetakahle

pandas/core/reshape/concat.py

@@ -347,19 +347,17 @@ def concat(
 
     Append a single row to the end of a ``DataFrame`` object.
 
-    >>> a = pd.DataFrame({"A": 1, "B": 2}, index=[0])
-    >>> a
-        A   B
+    >>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
+    >>> df7
+        a   b
     0   1   2
-    >>> b = pd.DataFrame({"A": 3}, index=[0])
-    >>> b
-        A
+    >>> new_row = pd.Series([3])
+    >>> new_row
     0   3
-    >>> for rowIndex, row in b.iterrows():
-    >>>     print(pd.concat([a, row.to_frame().T], ignore_index=True))
-       A    B
-    0  1  2.0
-    1  3  NaN
+    >>>     pd.concat([df7, new_row.to_frame().T], ignore_index=True)
+         a    b    0
+    0  1.0  2.0  NaN
+    1  NaN  NaN  3.0
     """
     op = _Concatenator(
         objs,

pandas/core/reshape/exaple.ipynb

@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "It is not recomended to build DataFrames by adding single rows in a\n",
+    "not loop. Build a list of rows and make a DataFrame in a single concat.\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   A  B\n0  1  2",
+      "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>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = pd.DataFrame({\"A\": 1, \"B\": 2}, index=[0])\n",
+    "a"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    3\ndtype: int64"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_row = pd.Series([3])\n",
+    "new_row"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "     A    B    0\n0  1.0  2.0  NaN\n1  NaN  NaN  3.0",
+      "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>A</th>\n      <th>B</th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([a, new_row.to_frame().T], ignore_index=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "# b = pd.DataFrame({\"A\": 3}, index=[0])\n",
+    "# b"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "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": 0
+}