Trying to remove the first row in python fails?

Hi I have some data in csv format, this is what the top of it looks like.

Hi I have some data in csv format, this is what the top of it looks like.

I have some very simple code that is just meant to remove the first row:

import numpy as np
import pandas as pd
data = pd.read_csv("mydata.csv")
data = data.drop(data.columns[[0]],axis=0)
data.to_csv("mydata2.csv")

However when run, I receieve this error:

Warning (from warnings module):
  File "C:/Users/george/Desktop/testing/output/PIVOTING.py", line 1
DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.

Traceback (most recent call last):
File "C:/Users/george/Desktop/testing/output/PIVOTING.py", line 5, in <module>
data = data.drop(data.columns[[0]],axis=0)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 3697, in drop
errors=errors)
File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 3111, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 3143, in _drop_axis
new_axis = axis.drop(labels, errors=errors)
File "C:\Python27\lib\site-packages\pandas\core\indexes\base.py", line 4404, in drop
'{} not found in axis'.format(labels[mask]))
KeyError: "['C'] not found in axis"


Python Tutorial – Learn Python 3 Programming from Experts

This Python tutorial will help you learn Python and build a career in this top programming language. Through this tutorial, you will learn Python basics, its salient features, basic syntax, variable types, operators, functions, modules, and more. You will also understand what exception handling is, how to access database, Python classes and loops, and ultimately how to write Python codes. Learn Python from Intellipaat Python Certification course and excel in your career!

Introduction to Python Programming


What is Python used for? Let’s begin with this Python tutorial in order to make you understand what Python programming is. We will start from Python basics.

As per Stack Overflow, Python language is one among the fastest growing programming languages in the world. Forbes says that Python language has seen the 7th largest increase in demand in the past few years. Due to this, there is an acute shortage of skilled and certified Python professionals. Hence, job opportunities for Python programming have increased, tremendously. According to Indeed, the average annual salary of a Python Developer in the US is $123,000.

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Watch this Python Tutorial Video for Beginners:


Python language is one of the most popular programming languages of the 21st century. It is a general-purpose language and has been here around for over 20 years. Python Syntax is also extremely simple and elegant. With its gentle and gradual learning curve, Python is considered as the best introductory programming language. I hope the introduction to python is clear to you by now.

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So, let’s jump onto the other topics in this python tutorial and learn python for beginners.

Here is the list of topics covered in this Python Tutorial, just in case you want to jump right into a specific one:

Why Python?
  • When it comes to automating the predictive model, Python language is the first choice for Data Scientists
  • Python language provides some of the awesome and robust libraries for Big DataArtificial Intelligence, Machine Learning, and Deep Learning.
  • For deploying Machine Learning models in production, Data Scientist prefers Python programming over R.
  • It is easy to integrate Python with Big Data frameworks such as Spark and Hadoop.
  • Python language has huge online community support.

What Can Python Do?

Python is a general-purpose language, which means that it can be used to write software applications in a variety of domains without being restricted to a particular domain. This feature sets Python apart from the domain-specific languages. Being a general-purpose language, Python can do a number of interesting things. Some of them are listed here in this python tutorial below:

  • Python programming can be used to write an application using Tkinterwhich use graphics rather than text to interact with the users.
  • Python language can be used to create games, using its modules such as Pygameor Kivy.
  • Python can also automate boring stuff such as sending emails, uploading status on a Facebook account, and more
  • Python can also be used to experiment with computer vision using its openCV library. It is particularly used in robotics to enable the robots to see and avoid obstacles while moving.
  • Drawing complex graphs and visualizing the data can be by Python, using its matplotliband similar libraries.
  • Being one of the top choices for data analysis, Python can also mine Twitter Data. How convenient is that! Especially, if you consider the fact that in today’s world there are literally hordes of data created every second.

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Difference Between Python 2 and Python 3

Since the release of the new version of Python, i.e., Python 3, which is explained the most in this Python tutorial, there has been a bit of debate in the coding community over deciding which Python version is better. Whether Python 3 is better than Python 2 is a rather subjective question, and the selection between the two eventually comes down to personal preference. But, one must know how Python 2 and Python 3 are different. Python 3 is a fundamentally different version from Python 2.

Check out all the key differences between Python 2 and 3:

Basis of the DifferencePython 2Python 3PrintIn Python 2, you treat print as a statement rather than as a function, and hence there is no need to put the text inside parentheses.In Python 3, you explicitly treat print as a function, which means the text you want to print has to be inside the parentheses or you will get a syntax error.Integer DivisionIn Python 2, the interpreter treats the numbers that you type without any decimal points as integers which might lead to unexpected outcomes of certain mathematical operations, e.g., 5/2 in Python 2 will give the result as 2 instead of 2.5. To get the result as 2.5, you will have to type 5.0/2.0.In Python 3, the interpreter automatically identifies the Python Data Types on the basis of the type of the value. So, the result of 5/2 in Python 3 is by default calculated as 2.5, which is way more convenient than Python 2.InputIn Python 2, you use the raw_input() function to take inputs from users. This function only returns the string representing the user’s input which you have to convert into the desired data type.In Python 3, the input() method is used to take inputs from the user. Here too, the type of the input is automatically interpreted in Python 3.String TypeThe implicit string type in Python 2 is ASCII.The implicit string type in Python 3 is Unicode.


Beginners’ Tips for Learning Python Programming Online

While learning as a beginner through this Python tutorial, you should follow these tips which will help you deal with some new concepts:

Tip #1: Practice Python every day

It is important to have consistency while learning any new programming language. It is important that you should make a commitment to write code every day, as it will play with your muscle memory which is a very important part in programming. Increase your level of coding gradually but never stop playing around with the Python basics coding part. You may end up learning something new every time you practice Python coding even about the python basics.

This python tutorial will also help you keep a good grasp of Python basics. Here are some examples for you to exercise:

Print the type of an element:

>> a = “this is a string”
>>> type(a)
>>> <class ‘str’> #output

Use docstring to add multi-line descriptive descriptions in your code:

>>def printOutput(str):
>>>“’This function will print the passed string’”
>>>print (str)
>>>return;

Watch this Python Tutorial Video for Beginners:



Import libraries:>>> import sklearn
>>> import pandas as pd
>>> import matplotlib.pyplot as plt

Perform operations and play around with them:

>> a = ‘Intellipaat’
>>> a.upper()
>>> ‘INTELLIPAAT’

The list does not end here, there are plenty other basic things that you can practice.

Tip #2: Use Interactive Python shell

Whether you are new to Python data structures (dictionaries, lists, strings, etc.) or you are debugging an application, the best learning tool is the Interactive Python Shell.

To use the Interactive Python Shell, first, you should install Python on your computer. This Python tutorial on the step-by-step Installation procedure of Python will help you learn how to install Python in your system. To activate and use the Interactive Python Shell, simply open the terminal on your system and run Python 2 or Python 3 depending on your version of installation. Once the shell is open, you can start writing the code.

Tip #3: Debug your code

Hitting a bug is normal when you start writing a complex program. Don’t get frustrated here; it happens with everyone! Instead, take these moments as pride and think of yourself as a bug bounty hunter. Debugging your own code will help you learn even more.

It is important to have a methodological approach while debugging the code, which will help you find where your code has broken down. Going through your code step by step in its executable order and making sure that each part of your code works fine is the way you should go about it.

Watch this Python Interview Questions video:


Tip #4: Try to build some small projects

Always try to make up a small project for each concept. This python tutorial will help you build confidence for writing Python programs, as well as this will help you develop the muscle memory. Once you have a solid foundation on the basic data structures (Python DictionariesPython StringsPython setsPython Lists, etc.), object-oriented programming, and writing classes, you will be ready to deploy these programming concepts in real life. Keep writing small codes to keep your concepts clear while learning through this Python Tutorial.

Tip #5: Contribute to open source

Python is an open-source language which means that its source code is available for the public to download, use, and modify. Anyone can collaborate and be an active member of Python community. You have access to the code written and produced by big companies. Working with these codes will be a very valuable learning experience. You can even reach out for other community members whenever you hit a bug in your coding.

For the best of career growth, check out Intellipaat’s Python Course in Sydney and get certified!

Intellipaat is providing free Python Interview Questions and Answers, which will help you excel in your career!

Originally published at www.intellipaat.com on August 24, 2019

The Pandas Library for Python

The Pandas Library for Python

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming ...

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming ...

  1. Introduction to Pandas
  2. About the Data
  3. Setup
  4. Loading Data
  5. Basic Operations
  6. The Dtype
  7. Cleansing and Transforming Data
  8. Performing Basic Operations
  • Calculations
  • Booleans
  • Grouping
  • Plotting
  1. Introduction to Pandas
  2. About the Data
  3. Setup
  4. Loading Data
  5. Basic Operations
  6. The Dtype
  7. Cleansing and Transforming Data
  8. Performing Basic Operations

Introduction to Pandas

So, what is Pandas — practically speaking? In short, it’s the major data analysis library for Python. For scientists, students, and professional developers alike, Pandas represents a central reason for any learning or interaction with Python, as opposed to a statistics-specific language like R, or a proprietary academic package like SPSS or Matlab. (Fun fact — Pandas is named after the term Panel Data, and was originally created for the analysis of financial data tables). I like to think that the final “s” stands for Series or Statistics.

Although there are plenty of ways to explore numerical data with Python out-of-the box, these will universally involve some fairly low-performance results, with a ton of boilerplate. It may sound hard to believe, but Pandas is often recommended as the next stop for Excel users who are ready to take their data analysis to the next level. Nearly any problem that can be solved with a spreadsheet program can be solved in Pandas — without all the graphical cruft.

More importantly, because problems can be solved in Pandas via Python, solutions are already automated, or could be run as a service in the cloud. Further, Pandas makes heavy use of Numpy, relying on its low-level calls to produce linear math results orders of magnitude more quickly than they would be handled by Python alone. These are just a few of the reasons Pandas is recommended as one of the first libraries to learn for all Pythonistas, and remains absolutely critical to Data Scientists.

About the Data

In this post, we’re going to be using a fascinating data set to demonstrate a useful slice of the Pandas library. This data set is particularly interesting as it’s part of a real world example, and we can all imagine people lined up at an airport (a place where things do occasionally go wrong). When looking at the data, I imagine people sitting in those uncomfortable airport seats having just found out that their luggage is missing — not just temporarily, but it’s nowhere to be found in the system! Or, better yet, imagine that a hardworking TSA employee accidentally broke a precious family heirloom.

So it’s time to fill out another form, of course. Now, getting data from forms is an interesting process as far as data gathering is concerned, as we have a set of data that happens at specific times. This actually means we can interpret the entries as a Time Series. Also, because people are submitting the information, we can learn things about a group of people, too.

Back to our example: let’s say we work for the TSA and we’ve been tasked with getting some insights about when these accidents are most likely to happen, and make some recommendations for improving the service.

Pandas, luckily, is a one-stop shop for exploring and analyzing this data set. Feel free to download the excel file into your project folder to get started, or run the curl command below. Yes, pandas can read .xls or .xlsx files with a single call to **pd.read_excel()**! In fact, it’s often helpful for beginners experienced with .csv or excel files to think about how they would solve a problem in excel, and then experience how much easier it can be in Pandas.

So, without further ado, open your terminal, a text editor, or your favorite IDE, and take a look for yourself with the guidance below.

Example data:

Take for example, some claims made against the TSA during a screening process of persons or a passenger’s property due to an injury, loss, or damage. The claims data information includes claim number, incident date, claim type, claim amount, status, and disposition.

Directory: TSA Claims Data

Our Data Download: claims-2014.xls

Setup

To start off, let’s create a clean directory. You can put this wherever you’d like, or create a project folder in an IDE. Use your install method of choice to get Pandas: Pip is probably the easiest.

$ mkdir -p ~/Desktop/pandas-tutorial/data && cd ~/Desktop/pandas-tutorial

Install pandas along with xldr for loading Excel formatted files, matplotlib for plotting graphs, and Numpy for high-level mathematical functions.

$ pip3 install matplotlib numpy pandas xldr

Optional: download the example data with curl:

$ curl -O https://www.dhs.gov/sites/default/files/publications/claims-2014.xls

Launch Python:

$ python3
Python 3.7.1 (default, Nov  6 2018, 18:46:03)
[Clang 10.0.0 (clang-1000.11.45.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

Import packages:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import pandas as pd

Loading Data

Loading data with Pandas is easy. Pandas can accurately read data from almost any common format including JSON, CSV, and SQL. Data is loaded into Pandas’ “flagship” data structure, the DataFrame.

That’s a term you’ll want to remember. You’ll be hearing a lot about DataFrames. If that term seems confusing — think about a table in a database, or a sheet in Excel. The main point is that there is more than one column: each row or entry has multiple fields which are consistent from one row to the next.

You can load the example data straight from the web:

>>> df = pd.read_excel(io='https://www.dhs.gov/sites/default/files/publications/claims-2014.xls', index_col='Claim Number')

Less cooly, data can be loaded from a file:

$ curl -O https://www.dhs.gov/sites/default/files/publications/claims-2014.xls

>>> df = pd.read_excel(io='claims-2014.xls', index_col='Claim Number')

Basic Operations

Print information about a DataFrame including the index dtype and column dtypes, non-null values, and memory usage. DataFrame.info() is one of the more useful and versatile methods attached to DataFrames (there are nearly 150!).

>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 8855 entries, 2013081805991 to 2015012220083
Data columns (total 10 columns):
Date Received    8855 non-null datetime64[ns]
Incident Date    8855 non-null datetime64[ns]
Airport Code     8855 non-null object
Airport Name     8855 non-null object
Airline Name     8855 non-null object
Claim Type       8855 non-null object
Claim Site       8855 non-null object
Item Category    8855 non-null object
Close Amount     8855 non-null object
Disposition      8855 non-null object
dtypes: datetime64[ns](2), object(8)
memory usage: 761.0+ KB

View the first n rows:

>>> df.info()
<class '>>> df.head(n=3)  # see also df.tail()
    Claim Number Date Received       Incident Date Airport Code       ...              Claim Site                   Item Category Close Amount      Disposition
0  2013081805991    2014-01-13 2012-12-21 00:00:00          HPN       ...         Checked Baggage  Audio/Video; Jewelry & Watches            0             Deny
1  2014080215586    2014-07-17 2014-06-30 18:38:00          MCO       ...         Checked Baggage                               -            0             Deny
2  2014010710583    2014-01-07 2013-12-27 22:00:00          SJU       ...         Checked Baggage                    Food & Drink           50  Approve in Full
[3 rows x 11 columns]

List all the columns in the DataFrame:

df.columns> df.columns> df.columns> df.columns> df.columns
Return a single column (important — also referred to as a Series):

>>> df['Claim Type'].head()
0    Personal Injury
1    Property Damage
2    Property Damage
3    Property Damage
4    Property Damage
Name: Claim Type, dtype: object

Hopefully, you’re starting to get an idea of what claims-2014.xls’s data is all about.

The Dtype

Data types are a fundamental concept that you’ll want to have a solid grasp of in order to avoid frustration later. Pandas adopts the nomenclature of Numpy, referring to a column’s data type as its dtype. Pandas also attempts to infer dtypes upon DataFrame construction (i.e. initialization).

To take advantage of the performance boosts intrinsic to Numpy, we need to become familiar with these types, and learn about how they roughly translate to native Python types.

Look again at df.info() and note the dtype assigned to each column of our DataFrame:

>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8855 entries, 0 to 8854
Data columns (total 11 columns):
Date Received    8855 non-null datetime64[ns]
Incident Date    8855 non-null datetime64[ns]
Airport Code     8855 non-null object
Airport Name     8855 non-null object
Airline Name     8855 non-null object
Claim Type       8855 non-null object
Claim Site       8855 non-null object
Item Category    8855 non-null object
Close Amount     8855 non-null object
Disposition      8855 non-null object
dtypes: datetime64[ns](2), object(8)
memory usage: 761.1+ KB

dtypes are analogous to text/number format settings typical of most spreadsheet applications, and Pandas uses dtypes to determine which kind(s) of operations may be performed the data in a specific column. For example, mathematical operations can only be performed on numeric data types such as int64 or float64. Columns containing valid Dates and/or time values are assigned the datetime dtype and text and or binary data is assigned the catchall object dtype.

In short, Pandas attempts to infer dtypes upon DataFrame construction. However, like many data analysis applications, the process isn’t always perfect.

It’s important to note that Pandas dtype inference errs on the side of caution: if a Series appears to contain more than one type of data, it’s assigned a catch-all dtype of ‘object’. This behavior is less flexible than a typical spreadsheet application and is intended to ensure dtypes are not inferred incorrectly but also requires the analyst to ensure the data is “clean” after it’s loaded.

Cleansing and Transforming Data

Data is almost always dirty: it almost always contains some datum with atypical formatting; some artifact unique to its medium of origin. Therefore, cleansing data is crucial to ensuring analysis derived therefrom is sound. The work of cleansing with Pandas primarily involves identifying and re-casting incorrectly inferred dtypes.

>>> df.dtypes
Date Received    datetime64[ns]
Incident Date    datetime64[ns]
Airport Code             object
Airport Name             object
Airline Name             object
Claim Type               object
Claim Site               object
Item Category            object
Close Amount             object
Disposition              object
dtype: object

Looking again at our DataFrame’s dtypes we can see that Pandas correctly inferred the dtypes of Date Received and Incident Date as datetime64 dtypes. Thus, datetime attributes of the column’s data are accessible during operations. For example, to summarize our data by the hour of the day when each incident occurred we can group and summarize our data by the hour element of a datetime64 column to determine which hours of the day certain types of incidents occur.

>>> grp = df.groupby(by=df['Incident Date'].dt.hour)
>>> grp['Item Category'].describe()
              count unique                   top freq
Incident Date
0              3421    146  Baggage/Cases/Purses  489
1                 6      5                 Other    2
2                11      9                     -    2
3                 5      5     Jewelry & Watches    1
4                49     18  Baggage/Cases/Purses    6
5               257     39                     -   33
6               357     54                     -   43
7               343     43              Clothing   41
8               299     47                     -   35
9               305     41                     -   31
10              349     45                 Other   43
11              343     41                     -   45
12              363     51                 Other   41
13              359     55                     -   45
14              386     60  Baggage/Cases/Purses   49
15              376     51                 Other   41
16              351     43  Personal Electronics   35
17              307     52                 Other   34
18              289     43  Baggage/Cases/Purses   37
19              241     46  Baggage/Cases/Purses   26
20              163     31  Baggage/Cases/Purses   23
21              104     32  Baggage/Cases/Purses   20
22              106     33  Baggage/Cases/Purses   19
23               65     25  Baggage/Cases/Purses   14

This works out quite perfectly — however, note that Close Amount was loaded as an ‘object’. Words like “Amount” are a good indicator that a column contains numeric values.

Let’s take a look at the values in Close Amount.

>>> df['Close Amount'].head()
0     0
1     0
2    50
3     0
4     0
Name: Close Amount, dtype: object

Those look like numeric values to me. So let’s take a look at the other end

>>> df['Close Amount'].tail()
8850      0
8851    800
8852      0
8853    256
8854      -
Name: Close Amount, dtype: object

There’s the culprit: index # 8854 is a string value.

If Pandas can’t objectively determine that all of the values contained in a DataFrame column are the same numeric or date/time dtype, it defaults to an object.

Luckily, I know from experience that Excel’s “Accounting” number format typically formats 0.00 as a dash, -.

So how do we fix this? Pandas provides a general method, DataFrame.apply, which can be used to apply any single-argument function to each value of one or more of its columns.

In this case, we’ll use it to simultaneously convert the — to the value it represents in Excel, 0.0 and re-cast the entire column’s initial object dtype to its correct dtype a float64.

First, we’ll define a new function to perform the conversion:

>>> def dash_to_zero(x):
>>>    if '-' in str(x):
>>>        return float() # 0.0
>>>    else:
>>>        return x  # just return the input value as-is

Then, we’ll apply the function to each value of Close Amount:

>>> df['Close Amount'] = df['Close Amount'].apply(dash_to_zero)
>>> df['Close Amount'].dtype
dtype('float64')

These two steps can also be combined into a single-line operation using Python’s lambda:

>>> df['Close Amount'].apply(lambda x: 0. if '-' in str(x) else x)

Performing Basic Analysis

Once you’re confident that your dataset is “clean,” you’re ready for some data analysis! Aggregation is the process of getting summary data that may be more useful than the finely grained values we are given to start with.

Calculations

>>> df.sum()
Close Amount    538739.51
dtype: float64

>>> df.min()
Date Received              2014-01-01 00:00:00
Incident Date              2011-08-24 08:30:00
Airport Code                                 -
Airport Name      Albert J Ellis, Jacksonville
Airline Name                                 -
Claim Type                                   -
Claim Site                                   -
Item Category                                -
Close Amount                                 0
Disposition                                  -

>>> df.max()
Date Received                       2014-12-31 00:00:00
Incident Date                       2014-12-31 00:00:00
Airport Code                                        ZZZ
Airport Name                 Yuma International Airport
Airline Name                                 XL Airways
Claim Type                              Property Damage
Claim Site                                        Other
Item Category    Travel Accessories; Travel Accessories
Close Amount                                    25483.4
Disposition                                      Settle
dtype: object

Booleans

Find all of the rows where ‘Close Amount’ is greater than zero. This is helpful because we’d like to see some patterins where the amount is actually positive, and show how conditional operators work.

>>> df[df['Close Amount'] > 0].describe()
       Close Amount
count   2360.000000
mean     228.279453
std      743.720179
min        1.250000
25%       44.470000
50%      100.000000
75%      240.942500
max    25483.440000

Grouping

In this example, we’ll walk through how to group by a single column’s values.

The Groupby object is an intermediate step that allows us to aggregate on several rows which share something in common — in this case, the disposition value. This is useful because we get a birds-eye view of different categories of data. Ultimately, we use describe() to see several aggregates at once.

>>> grp = df.groupby(by='Disposition')
>>> grp.describe()
                Close Amount
                       count        mean          std   min       25%      50%       75%       max
Disposition
-                     3737.0    0.000000     0.000000  0.00    0.0000    0.000    0.0000      0.00
Approve in Full       1668.0  158.812116   314.532028  1.25   32.9625   79.675  159.3375   6183.36
Deny                  2758.0    0.000000     0.000000  0.00    0.0000    0.000    0.0000      0.00
Settle                 692.0  395.723844  1268.818458  6.00  100.0000  225.000  425.6100  25483.44

Group by multiple columns:

>>> grp = df.groupby(by=['Disposition', 'Claim Site'])
>>> grp.describe()
                                Close Amount
                                       count         mean          std     min       25%       50%        75%       max
Disposition     Claim Site
-               -                       34.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Bus Station              2.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Checked Baggage       2759.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Checkpoint             903.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Motor Vehicle           28.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Other                   11.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
Approve in Full Checked Baggage       1162.0   113.868072   192.166683    1.25   25.6600    60.075   125.9825   2200.00
                Checkpoint             493.0   236.643367   404.707047    8.95   60.0000   124.000   250.1400   6183.36
                Motor Vehicle            9.0  1591.428889  1459.368190  493.80  630.0000   930.180  1755.9800   5158.05
                Other                    4.0   398.967500   358.710134   61.11  207.2775   317.385   509.0750    899.99
Deny            -                        4.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Checked Baggage       2333.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Checkpoint             407.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
                Motor Vehicle            1.0     0.000000          NaN    0.00    0.0000     0.000     0.0000      0.00
                Other                   13.0     0.000000     0.000000    0.00    0.0000     0.000     0.0000      0.00
Settle          Checked Baggage        432.0   286.271968   339.487254    7.25   77.0700   179.995   361.5700   2500.00
                Checkpoint             254.0   487.173031  1620.156849    6.00  166.9250   281.000   496.3925  25483.44
                Motor Vehicle            6.0  4404.910000  7680.169379  244.00  841.8125  1581.780  2215.5025  20000.00

Plotting

While aggregates on groups of data is one of the best ways to get insights, visualizing data lets patterns jump out from the page, and is straightforward for those who aren’t as familiar with aggregate values. Properly formatted visualizations are critical to communicating meaning in the data, and it’s nice to see that Pandas has some of these functions out of the box:

>>> df.plot(x='Incident Date', y='Close Amount')
>>> plt.show()

Incident Date by Close Amount

Exporting Transformed Data

Finally, we may need to commit either our original data, or the aggregates as a DataFrame to file format different than the one we started with, as Pandas does not limit you to writing back out to the same file format.

The most common flat file to write to from Pandas will be the .csv. From the visualization, it looks like the cost of TSA claims, while occasionally very high due to some outliers is improving in 2015. We should probably recommend comparing staffing and procedural changes to continue in that direction, and explore in more detail why we have more incidents at certain times of day.

Like loading data, Pandas offers a number of methods for writing your data to file in various formats. Writing back to an Excel file is slightly more involved than the others, so let’s write to an even more portable format: CSV. To write your transformed dataset to a new CSV file:

>>> df.to_csv(path_or_buf='claims-2014.v1.csv')

Final Notes

Here we’ve seen a workflow that is both interesting and powerful. We’ve taken a round-trip all the way from a government excel file, into Python, through some fairly powerful data visualization, and back to a .csv file which could be more universally accessed — all through the power of Pandas. Further, we’ve covered the three central objects in Pandas — DataFrames, Series, and dtypes. Best of all, we have a deeper understanding of an interesting, real-world data set.

These are the core concepts to understand when working with Pandas, and now you can ask intelligent questions (of yourself, or of Google) about these different objects. This TSA data use case has shown us exactly what Pandas is good for: the exploration, analysis, and aggregation of data to draw conclusions.

The analysis and exploration of data is important in practically any field, but it is especially useful to Data Scientists and AI professionals who may need to crunch and clean data in very specific, finely-grained ways, like getting moving averages on stock ticks. Additionally, certain tasks may need to be automated, and this could prove difficult or expensive in sprawling applications like Excel, or Google Sheets, which may not offer all the functionality of Pandas with the full power of Python.

Just imagine telling a business administrator that they may never have to run that broken spreadsheet macro ever again! Once analysis is automated, it can be deployed as a service or applied to hundreds of thousands of records streaming from a database. Alternatively, Pandas could be used to make critical decisions after establishing statistical associations between patterns, as indeed it is every day.

Next, be sure to checkout at Python’s extensive database libraries (e.g. SQLalchemy), or API clients (like the Google Sheets/Slides Python Client or Airtable API to put your results in front of domain experts). The possibilities are endless, and are only enhanced by Python’s mature libraries and active community.

Thanks for reading ❤

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Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Description
Learn Hands-On Python Programming By Creating Projects, GUIs and Graphics

Python is a dynamic modern object -oriented programming language
It is easy to learn and can be used to do a lot of things both big and small
Python is what is referred to as a high level language
Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps!
SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily
If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing Project and GUIs.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

When and how do I start a career as a Python programmer?

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

Python Job Profiles
Software Engineer
Research Analyst
Data Analyst
Data Scientist
Software Developer
Python Salary

The median total pay for Python jobs in California, United States is $74,410, for a professional with one year of experience
Below are graphs depicting average Python salary by city
The first chart depicts average salary for a Python professional with one year of experience and the second chart depicts the average salaries by years of experience
Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis
Take this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future! Best of Luck
Who is the target audience?

Anyone who wants to learn to code
For Complete Programming Beginners
For People New to Python
This course was designed for students with little to no programming experience
People interested in building Projects
Anyone looking to start with Python GUI development
Basic knowledge
Access to a computer
Download Python (FREE)
Should have an interest in programming
Interest in learning Python programming
Install Python 3.6 on your computer
What will you learn
Build Python Graphical User Interfaces(GUI) with Tkinter
Be able to use the in-built Python modules for their own projects
Use programming fundamentals to build a calculator
Use advanced Python concepts to code
Build Your GUI in Python programming
Use programming fundamentals to build a Project
Signup Login & Registration Programs
Quizzes
Assignments
Job Interview Preparation Questions
& Much More