#pivot_table

[markcraft3636]

Learn MS Access |Beginners Course |Working with Data |Pivot Tables |Enhancing Pivot Tables

Microsoft Access is one of the products from Creative Cloud that allows you to create, manage and operate with table databases, queries, forms and extract data with calculated fields, macros, modules and great reports. In this tutorial we are going to see all the main features you need to know to start working with data , Creating and working with Pivot Tables , including how to enhance Pivot Tables. Pivot table in Access is a good way to group and summarize huge amounts of information like sales records pulled from a database. Pivot tables allow you to analyze your data in a more graphical manner. This Tutorial is available Free of Cost and lots more Courses are about to come. Stay Connected and get our new series of Full Fledged Courses.

[learning-path]

Reshaping and Pivot Tables in Pandas

Unlock the power of data manipulation with Pankaj's latest blog! 🚀 Discover the secrets of reshaping data and mastering pivot tables using Pandas in Python. 🐍 Dive in now and level up your data skills! 💡 #DataScience #Python #Pandas #DataManipulation

In this codes with Pankaj blog , we’ll explore how to reshape data and create pivot tables using Pandas, a powerful data manipulation library in Python. We’ll cover various methods such as pivot(), pivot_table(), stack(), unstack(), melt(), wide_to_long(), get_dummies(), from_dummies(), explode(), crosstab(), cut(), and factorize(). 1. pivot() and pivot_table() 2. stack() and unstack() 3.…

[codewithnazam]

Pandas DataFrame Pivot Table: Unlocking Efficient Data Analysis Techniques

Level up your data game with Pandas pivot tables! This guide shows you how to reshape data, uncover secrets, and impress your boss. 🪄 Let's get pivoting! #datascience #datasummary #pythontips

The Pandas DataFrame Pivot Table is a fundamental tool in data manipulation and analysis, allowing data professionals to restructure and summarize data for enhanced insights. By using the pivot_table() function within the Pandas library, users can aggregate and pivot their datasets based on specific parameters, resulting in a clearer visualization of the relationships within the data. This…

[ms-powerbi]

Learn MS Access -#1|Beginners Course |Working with Data |Pivot Tables |Enhancing Pivot Tables 

Microsoft Access is one of the products from Creative Cloud that allows you to create, manage and operate with table databases, queries, forms and extract data with calculated fields, macros, modules and great reports. In this tutorial we are going to see all the main features you need to know to start working with data , Creating and working with Pivot Tables , including how to enhance Pivot Tables. Pivot table in Access is a good way to group and summarize huge amounts of information like sales records pulled from a database. Pivot tables allow you to analyze your data in a more graphical manner.

This Tutorial is available Free of Cost and lots more Courses are about to come. Stay Connected and get our new series of Full Fledged Courses.

#access , #access2019, #msaccess, #markcraftsolution , # Youtube , #trending , #freetutorial , #data_entry#dataentry#data_entry_in_access#data_entry_work_in_access#access_data_entry_form , #pivot_table , #pivot_table_in_access,

[msamary]

How to Refresh a Pivot Table in Microsoft Excel: Top 3 Methods http://dlvr.it/ST1LLb

[studiodoli]

【Pythonメモ】 pandas pivot_table編

pandas.pivot_table(data, index=, columns=, )

　data: pandas.dataFrameオブジェクト

data.pivot_table(index=, columns=, ) もOK？

-----------------------

引数 aggfunc - 結果値の算出方法（デフォルトは平均値 numpy.mean() ） 　‘sum‘ : 合計 　‘size’：クロス集計（列と行が同時に発生する頻度） 　len：件数 　np.sum：合計 　np.max：最大 　np.min：最小 　np.median：中央値 　np.mean：平均値（デフォルト） 　np.std：標準偏差

------------------------

引数 margins - カテゴリごとの小計・総計を算出 　True：小計算出 　※ 引数 margins_name でラベル指定可能（デフォルト “All”）

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引数 dropna - 欠損値NaNの除外指定

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引数 values - 算出対象指定

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引数 fill_value - 欠損値を置き換える 　例：fill_value=0

[drifting-blue]

also pandas is just so inconsistent with its method and argument naming conventions im gonna scream

hey codeinstitute this is a terrible way to teach pandas

[santoshbisp]

Pandas Pivot | Python Pandas Apply Pivot | Business Data Analysis using Pandas

Pandas pivot tables are used to group similar columns to find totals, averages, or other aggregations. You can accomplish this same functionality in Pandas with the pivot_table method.

[pritam646]

To learn #Microsoft_Excel #VBA, #Dashboards, #Functions, #Formulas, #Pivot_Tables, You can check this #FREE #Course.

http://bit.ly/VBA_Series_Technical_Portal

[markcraft3636]

How to use MS Access-#1|Beginners Course |Working with Data |Pivot Tables |Enhancing Pivot Tables - YouTube

Microsoft Access is one of the products from Creative Cloud that allows you to create, manage and operate with table databases, queries, forms and extract data with calculated fields, macros, modules and great reports.

In this tutorial we are going to see all the main features you need to know to start working with data , Creating and working with Pivot Tables , including how to enhance Pivot Tables.

Pivot table in Access is a good way to group and summarize huge amounts of information like sales records pulled from a database. Pivot tables allow you to analyze your data in a more graphical manner.

#Access , #Access2019, #MSAccess, #markcraftsolution , # Youtube , #trending , #freetutorial , #data_entry #dataentry #data_entry_in_access #data_entry_work_in_access #access_data_entry_form , #pivot_table , #pivot_table_in_access,

[bionicly-blog]

Data Insights Case Study Part 2 - More Preprocessing

In the last couple posts, we’ve:

Identified the problem / hypothesis to go after.

Figured out the data set to work with.

Figured out a subset of the data to load into a relational DB (and structured in a roughly “standardized” format).

Connected the data in the relational DB to frontend Python and showing the data as a Pandas dataframe.

Now we have to do more work. If you even give a cursory look at edu_indicators_f and econ_indicators data (or the tables in the relational DB), you’ll notice how the data is uneven and not ready for an algorithm like logistic regression to operate on them:

Different potential parameters and potential target variables have different volumes in the data

There are a LOT of such parameters and target variables (132 economic indicators and 23 education indicators)

The years have different ranges for different parameters/variables (although most end around 2014).

The values have a wide range, and are likely not comparable and would distort algorithms that try to extract insight out of them.

For example, run these queries:

select distinct indicatorcode, count(*) as volume from econ_indicators group by indicatorcode order by volume desc;

select distinct indicatorcode, count(*) as volume from edu_indicators_f group by indicatorcode order by volume desc;

select distinct(indicatorcode), min(year), max(year) from econ_indicators group by indicatorcode

select distinct(indicatorcode), min(year), max(year) from edu_indicators_f group by indicatorcode

select distinct(indicatorcode), min(val), max(val) from edu_indicators_f group by indicatorcode

You’ll see some results such as:

The challenge, then, is what do we do about it. There are many ways to tackle this but this is how I approached it:

First, the target variables. There are 132 economic indicators, so it’s not very practical to try figure out the relationship between the education factors and ALL the economic indicators. We’ll have to filter down to a few candidates that covers the most number of countries and the most number of years:

select distinct(indicatorcode), count(distinct(countrycode)) as numcountries, count(distinct(year)) as numyears from econ_indicators group by indicatorcode order by numcountries desc, numyears desc;

Obviously, some intuition/judgement need to be made here to pick the top features, or it could be done through a threshold (e.g., numcountries >= 230). For purposes of this initial data exploration and not having to write 2313414 posts, we’ll stick to NY.GDP.MKTP.CD for now, as the target variable, and can always revisit later.

create table econ_target as select indicatorcode, countrycode, year, val from econ_indicators where indicatorcode = 'NY.GDP.MKTP.CD'

Second, for the features (educational indicators), we don’t need all of them either. Because it’s a much smaller set, we can probably use downstream algorithms like PCA to figure out which subset best explains the values in the target variables. But for sake of this initial exploration (and again, not having to write 2313414 posts ;)), we’ll arbitrarily limit it to at least 230 countries and 45 years. We can always come back and all the rest back in for more in-depth exploration.

select distinct(indicatorcode), count(distinct(countrycode)) as numcountries, count(distinct(year)) as numyears from edu_indicators_f group by indicatorcode order by numcountries desc, numyears desc

create table edu_candidates as select indicatorcode, countrycode, year, val from edu_indicators_f where indicatorcode in ('SE.PRM.ENRL.FE.ZS', 'SE.SEC.ENRL.GC.FE.ZS', 'SE.SEC.ENRL.FE.ZS', 'SE.PRM.TCHR.FE.ZS', 'SE.SEC.ENRL.VO.FE.ZS', 'SE.SEC.TCHR.FE.ZS', 'SE.SEC.TCHR.FE')

Third, for both education and economic indicators, we don’t need the country name or the indicator name, nor the ID columns. Rather, we can drop these and rely on the code columns. When we need to identify what the actual variables are, we can always use the original table mappings to retrieve the full names. To make downstream (e.g., Python side) processing easier, let’s consolidate both candidate feature and target variables in one table and join on the country and years. Also, since we don’t care about what the target variable is called (it’s always 'NY.GDP.MKTP.CD' in our case), we can drop that as well:

create table ed_ec_trans as select ed.year as yr, ed.countrycode as country, ed.indicatorcode as edu_ind, ed.val as edu_val, ec.val as ec_val from econ_target as ec, edu_candidates as ed where ed.countrycode = ec.countrycode and ec.year = ed.year;

Fourth, we need to read it into the Python side (using Pandas), and then reformat it so that all the variables in edu_ind are turned into columns (features), to make it easier to process downstream. If you need a primer on how pivot tables work in Pandas, check this out. Now, assuming you’re using InsightTools class that we defined in the previous post, replace the credentials with your own:

def main():

    iTools = InsightTools('localhost', '<your database>', '<your user>', '<your password>');       df = iTools.fetch_data_as_pd('select * from ed_ec_trans limit 200');

      dfp = pd.pivot_table(df,index=['yr', 'country', 'ec_val'], columns=['edu_ind'], fill_value=0, aggfunc=‘sum’)

      dfp.to_csv('dfp.csv')

Note the limit 200 and the CSV file. This is so that you can quickly test and check whether the format of the data is what you expect...and while you can print to screen to check, with large dataframes (even with something like Python’s tabulate) it becomes harder to read. But limit the results and export to CSV and then you can view it nicely in Excel or Libreoffice (if you’re on Linux like me). Also, don’t forget the aggfunc=‘sum’ since by default, pivot_table will try to aggregate by averaging the values...and while that may be ok, we don’t want to do computations in our data just yet as we’ll do quite a bit of these in the next post.

Now, If you look closely at the CSV file as shown in Libreoffice, you’ll see how ec_val is in the index section as a level (our target variable) and all the edu_val variables on the right of it are the feature columns that we’ll deal with in the next post.

Before we can use the dataframe further, we need to pull the target variable out of the index and onto its own column:

dfp = dfp.reset_index(level=['ec_val'])

If you export to CSV again, you’ll see how ec_val is now its own column and not part of the index:

We can leave the year and country as part of the index.

Fifth, we need to scale the values in the feature and target columns, as we don’t want certain features to otherwise distort the downstream algorithms. There are many ways to scale, but to start with (and keeping in mind we may do PCA later on), we’ll use standardization. And we’ll also move up the 2nd-level education column names to first level and name the economic indicator variable (the ‘y’) variable as well, so we can more easily refer to all these later:

from sklearn.preprocessing import StandardScaler;

scaler = StandardScaler()

dfp_std = pd.DataFrame(scaler.fit_transform(dfp), columns = dfp.columns)

dfp_std.to_csv('dfp_std.csv')

dfp_std.columns = [col[1] for col in dfp_std.columns]

dfp_std.columns.values[0] = 'ec_ind'

You’ll see the the dataframe that’s almost ready for downstream processing when you open the CSV file:

Finally, did you notice something we did earlier that we should undo? ;) If you’ve been paying close attention, we need to remove the “limit 200″ and then run the script again, to pull in and preprocess the entire dataset:

df = iTools.fetch_data_as_pd('select * from ed_ec_trans');

In the next post, we’ll start the fun stuff to actually clean insight from the preprocessed data.

[ms-powerbi]

Learn MS Access |Beginners Course |Working with Data |Pivot Tables |Enhancing Pivot Tables

Microsoft Access is one of the products from Creative Cloud that allows you to create, manage and operate with table databases, queries, forms and extract data with calculated fields, macros, modules and great reports. In this tutorial we are going to see all the main features you need to know to start working with data , Creating and working with Pivot Tables , including how to enhance Pivot Tables. Pivot table in Access is a good way to group and summarize huge amounts of information like sales records pulled from a database. Pivot tables allow you to analyze your data in a more graphical manner.

This Tutorial is available Free of Cost and lots more Courses are about to come. Stay Connected and get our new series of Full Fledged Courses.

Access , #Access2019, #MSAccess, #markcraftsolution , # Youtube , #trending , #freetutorial , #data_entry #dataentry #data_entry_in_access #data_entry_work_in_access #access_data_entry_form , #pivot_table , #pivot_table_in_access,

[markcraft3636]

How to use MS Access-#1|Beginners Course |Working with Data |Pivot Tables |Enhancing Pivot Tables - YouTube

Microsoft Access is one of the products from Creative Cloud that allows you to create, manage and operate with table databases, queries, forms and extract data with calculated fields, macros, modules and great reports.

In this tutorial we are going to see all the main features you need to know to start working with data , Creating and working with Pivot Tables , including how to enhance Pivot Tables.

Pivot table in Access is a good way to group and summarize huge amounts of information like sales records pulled from a database. Pivot tables allow you to analyze your data in a more graphical manner.

#Access , #Access2019, #MSAccess, #markcraftsolution , # Youtube , #trending , #freetutorial , #data_entry #dataentry #data_entry_in_access #data_entry_work_in_access #access_data_entry_form , #pivot_table , #pivot_table_in_access,

[pritam646]

FREE Course Microsoft Excel

To learn #Microsoft_Excel #VBA, #Dashboards, #Functions, #Formulas, #Pivot_Tables, You can check this #FREE #Course.

http://bit.ly/VBA_Series_Technical_Portal