From c84405f99d196297e7e15e8241e4ce341f8acf7a Mon Sep 17 00:00:00 2001
From: rotuna <sadhanasrinivasan@protonmail.com>
Date: Mon, 8 Oct 2018 16:52:59 +0530
Subject: [PATCH] DOC: Added example for Excel Filters and Fill handle

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 doc/source/comparison_with_excel.rst | 121 +++++++++++++++++++++++++++
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+.. currentmodule:: pandas 
+.. _compare_with_excel:
+
+.. ipython:: python
+    :suppress:
+
+    import pandas as pd 
+    import random
+    pd.options.display.max_rows=15
+
+Comparison with Excel
+*********************
+
+Commonly used Excel functionalities
+-----------------------------------
+
+Fill Handle
+~~~~~~~~~~~
+
+Create a series of numbers following a set pattern in a certain set of cells. In
+Excel this would be done by shift+drag after entering the first number or by 
+entering the first two or three values and then dragging.
+
+This can be achieved by creating a series and assigning it to the desired cells.
+
+.. ipython:: python
+
+    df = pd.DataFrame({'AAA': [1] * 8, 'BBB': list(range(0, 8))}); df
+
+    series = list(range(1, 5)); series
+
+    df.iloc[2:(5+1)].AAA = series
+
+    df
+
+Filters
+~~~~~~~
+
+Filters can be achieved by using slicing. 
+
+The examples filter by 0 on column AAA, and also show how to filter by multiple
+values.
+
+.. ipython:: python
+
+   df[df.AAA == 0]
+
+   df[(df.AAA == 0) | (df.AAA == 2)]
+
+
+Drop Duplicates
+~~~~~~~~~~~~~~~
+
+Another commonly used function is Drop Duplicates. This is directly supported in 
+pandas.
+
+.. ipython:: python
+
+    df = pd.DataFrame({"class": ['A', 'A', 'A', 'B', 'C', 'D'], "student_count": [42, 35, 42, 50, 47, 45], "all_pass": ["Yes", "Yes", "Yes", "No", "No", "Yes"]})
+
+    df.drop_duplicates()
+
+    df.drop_duplicates(["class", "student_count"])
+
+
+Pivot Table
+~~~~~~~~~~~
+
+This can be achieved by using ``pandas.pivot_table`` for examples and reference, 
+please see `pandas.pivot_table <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html>`__ 
+
+
+Formulae
+~~~~~~~~
+
+Let's create a new column "girls_count" and try to compute the number of boys in 
+each class. 
+
+.. ipython:: python 
+
+    df["girls_count"]  = [21, 12, 21, 31, 23, 17]; df
+
+    def get_count(row):
+        return row["student_count"] - row["girls_count"]
+
+    df["boys_count"] = df.apply(get_count, axis = 1); df
+
+
+VLOOKUP
+~~~~~~~
+
+.. ipython:: python
+
+    df1 = pd.DataFrame({"keys": [1, 2, 3, 4, 5, 6, 7], "first_names": ["harry", "ron",
+    "hermione", "rubius", "albus", "severus", "luna"]}); df1
+
+    random_names = pd.DataFrame({"surnames": ["hadrid", "malfoy", "lovegood",
+    "dumbledore", "grindelwald", "granger", "weasly", "riddle", "longbottom",
+    "snape"], "keys": [ random.randint(1,7) for x in range(0,10) ]})
+
+    random_names
+
+    random_names.merge(df1, on="keys", how='left')
+
+Adding a row
+~~~~~~~~~~~~
+
+To appended a row, we can just assign values to an index using ``iloc``. 
+
+NOTE: If the index already exists, the values in that index will be over written.
+
+.. ipython:: python
+
+    df1.iloc[7] = [8, "tonks"]; df1
+
+
+Search and Replace
+~~~~~~~~~~~~~~~~~~
+
+The ``replace`` method that comes associated with the ``DataFrame`` object can perform 
+this function. Please see `pandas.DataFrame.replace <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html>`__ for examples.