DOC: update cheatsheet
- [x] closes #39724
- [x] tests added / passed  (not required)
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them  (not required)
- [x] whatsnew entry (not required)



#### Major changes

1) **Replaced Heading "Data Wrangling ..." with section "General"**
   - I found it useful to have the hyperlinks to pandas api reference and user guide included
   - Also, all the time time when you work with pandas you need other packages (matplotlib etc.). That's why added hyperlinks for them as well
   - There is some place left for other useful links!
2) **Replaces explanatory section "Tidy data" with  section "Display & Visualize data"**
   - Cheat sheets are for users who already have basic knowledge. So this explanation of Tidy Data is of no use here
   - Displaying and visualizing data is one of the main tasks. So this section should be included in the cheat sheet
   - On the previous cheat sheet there also was a section "Plotting". But it was very small at the end of the second page.
3) **Added section "Apply Funtions"** (bottom of second page)
   - There are many ways to apply functions and I always find it confusing to remember them. So I added them here with a short description
   - However, if you have a better suggestion for this section, it can be interchanged
4) **Refractor section "Subset Observation"**
   - This section was pretty confusing in styling and layout before. E.g. there where multiple places where you can find the df.loc[]  and df.iloc [] functions.
   - I reordered this section
   - I added df.iloc[row_ind, column_ind] and df.loc[row_ind, column_ind]

Let me know what you think about it and make suggestions.
Oli
1. - [x] done (sorry this if obvious - I should have known it!)
2. - [x] done
3. - [x] done
4. - [x] Done. However, I don't know why you are keen on having it there. People using pandas know this. Or am I mistaken / missing something?
5. -  [ ] I partly agree. Thing is, I found it confusing to have the same function at multiple locations when using the sheet for the first times. More importantly, I think it is very useful to have multiple examples of `loc `and `iloc`, because they are so versatile. I made a suggestion for this section. Let me know what you think. 
6. - [x] Not sure what you mean. Is this alright?
7. - [x] done
8. - [x] done
9. - [x] done

I havent had time yet, but I agree with that third page. Will work on it later.
- [ ] ToDo: Adding third page

 Idea: when we have a third page, there will be enough space. So why don't we create a section - I don't mind on which page - "Design Principles" or "Best Practice". Within this section we can have the comment about tidy data, vectorization and also things such as "First column is 0". This pull request is stale because it has been open for thirty days with no activity. Please update or respond to this comment if you're still interested in working on this. First, sorry for the delay on reviewing this.  It slipped through the proverbial cracks.

>4.   Done. However, I don't know why you are keen on having it there. People using pandas know this. Or am I mistaken / missing something?

Some people start using pandas by using the cheat sheet, so that's why I like putting the stuff at the top.  Also, it maps the words observation to row and variable to column, which corresponds to wording in the statistics community.

> 5.  I partly agree. Thing is, I found it confusing to have the same function at multiple locations when using the sheet for the first times. More importantly, I think it is very useful to have multiple examples of `loc `and `iloc`, because they are so versatile. I made a suggestion for this section. Let me know what you think.

We're getting close.   See below.

> 6.   Not sure what you mean. Is this alright?

No.  `df.shape` is like `len(df)`.  I think you can make a new line for `df.shape` and describe it as "Tuple of # of rows, # of columns in DataFrame"

Now, for other comments:

1. On first page, let's use the white space below "Subset Variables - columns" as a new section "Using query", and remove `df.query(expr)` from the Subset Observations section , but provide a few examples in the new section.  Here are 3 examples to use
   *  `df.query("Length > 7")` 
   * `df.query("Length > 7 and Width < 8")`
   * `df.query("Name.str.startswith('abc')", engine="python")`
2. On first page, for `.iloc` and `.loc` provide examples rather than using `row_ind` and `column_ind`
3. On second page, in the "Group Data" section, in the `groupby` example, remove the `.mean()`


 Alright, I adapted everything, apart from: 

> 2. On first page, for .iloc and .loc provide examples rather than using row_ind and column_ind

For me, it was not obvious at first that the first index is `row_ind` and the second is `column_ind`. I always confused them and had to look it up quite often.

I removed `row_ind` and `column_ind` and added the sentence: `First index selects rows, second index columns.`
 @OliEfr We're getting even closer  Additional changes to make:
1. In the `query` examples, make sure the quotes are straight quotes, not curly quotes
2. Have a hyperlink from each `query` example to the docs
3. In the blue box for `query`, make it smaller, then have text before the examples that says "query() allows boolean expressions for filtering rows".  To help make space, change the text for the `df[['width', 'length', 'species']]` examples to put the word "names" on the same line as the rest of the text.
4. In the examples for `loc` and `iloc`, add hyperlinks to the docs
5. This is a small thing, but on page 1, the box for "Creating DataFrames" should top align with the box for "Reshaping Data".  If you move "Creating DataFrames" dow n a bit, you can increase the font size in the "Data Wrangling with pandas Cheat Sheet" that was closer to the old font size.
6. Since there is now some space at the top of the box with the "Creating DataFrames" example, spread out the examples vertically to distribute the white space
7. In the `df.iat[1,2]` example, add a space after the comma. Check Should have caught a few more issues (some of which existed prior to your PR):

1. On page 2, in Group Data, in first example, get rid of `.max()` 
2. In the `df.iloc` and `df.loc` examples, make sure there are spaces after every comma
3. Add spaces after commas in the last examples with `MultiIndex`.  If the alignment isn't great, consider splitting the tuples over multiple lines, which you can now do because you have the white space available Check - and corrected a few more comma and spacing issues. Thanks @OliEfr  If you want, you can create an issue with your ideas for a third page/further ideas.

I'll probably keep working on it. > If you want, you can create an issue with your ideas for a third page/further ideas.
> 
> I'll probably keep working on it.

Good idea.  Created #40680 

If you do that work, I'll try to be more responsive!
 ### changes
for update cheatsheet issue  #39806
**1.replace the heading Data Wrangling to Shortcuts for DATASCIENCE tools**
-  insert some referral links at the top of the sheet for easy to understand
- [https://www.w3schools.com/python/pandas/default.asp]
- 
### remake as simple learning Data Frames how to read the data from the csv file:
*for example
1. `from sklearn.datasets import load_iris
import pandas as pd

data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()`

![image](https://user-images.githubusercontent.com/47246236/131485509-ebca93cc-4c1b-4340-b048-851146d7589c.png)

### 2.subset variables / observations insert some python codes useful for pandas data frames and updates 
1. better introduce 
2.. `# Select the species and plot columns from the DataFrame
surveys_df[['species_id', 'plot_id']]`

# Select the last element in the list
# (the slice starts at the last element, and ends at the end of the list)
surveys_df[-1:]

# Using the 'copy() method'
true_copy_surveys_df = surveys_df.copy()
# Using the '=' operator
ref_surveys_df = surveys_df

###3.Summarizing Data and Handling the missing Data:
# summarize the dataset
print(dataset.describe())
# count the number of missing values for each column
num_missing = (dataset[[1,2,3,4,5]] == 0).sum()
# report the result

 > ### changes
> for update cheatsheet issue #39806

@maripisravankumar This is a closed PR on the cheat sheet.  If you would like to propose other changes to the cheat sheet, you can create your own PR and do edits, and I will either accept or reject them, or suggest changes.
