Wrangling Data with Pandas

According to a survey, 54% of the respondents use Python as their data science tool. Pandas is an open-source software library used for datasets manipulation and analysis in python

Whenever we think about Data Science, we probably think about a lot of data, analysis, statistics and so on. Pandas is an open-source software library used for datasets manipulation and analysis. It provides the data structure to manipulate tables and time series. The term Panda came from the “patent data”, which signifies the data encountered in econometrics and statistics.

According to a survey, 54% of the respondents use Python as their data science tool. There is no doubt that Python is on the verge of defining the future of the enterprises. The way it is matching the speed that certainly shows that it is not going to slow down soon. The demand of python professional is growing day by day so if you want to grab the opportunity, you need to enroll into Data Science with Python Course, such that you become the part of this lucrative field.

Sometimes, Data wrangling is also known as Data Munging. It is the process of converting the data from one form to another, with the purpose of making it more valuable and appropriate for purposes like analytics.

To install the Pandas library using pip, write the following code:

pip install pandas

then to import write

import pandas as pd

Pandas are commonly used for CSV files. To read a CSV file as dataframe, write

pd.read_csv

The command will load the data into a dataframe. For the glimpse of data use head() in the dataframe.

df = pd.read_csv
print(df.head())

Usually, the data frame consists of rows and columns of data which is known as a series, in pandas. describe() is one of the interesting function in data frames which shows the table of statistics. The command is useful to check the sanity dataset, check for the distribution of data and to check how much it matches with your expectation level.  

For instance, if you want to shuffle your data and wants to avoid buffering while taking out the data, then Pandas will be the best option for extra data mixing. Although, the data which doesn’t fit into the memory, of course, it needs a highly developed approach.

Wrangling also involves data processing in a different format such as grouping, concatenation, and merging. To achieve the analytical goal python has this feature inbuilt for data wrangling in various datasets.

Till here, you must have got some instinct about what Pandas is and what it does? It is way more flexible, expressive and fast than it seems at first. There is much more functionality in data wrangling package, it’s just you need to explore more, rearrange or reshape, data visualization, Data Frames iteration, and much more. However, to explore more about basic will be far more complex than mastering.

Pandas Cheat Sheet is a quick guide which depicts the purpose of pandas and takes your hands in-depth of data wrangling implementing python. The sheet will mentor you in learning the advanced indexing techniques, handling missing or repeating values, data functionality, data iteration and data visualization. In short, everything needed for data manipulation.

Wrapping Up:

Python is gaining popularity day by day among the industries. Due to its fully fledged language and other features such as data analysis, faster manipulation, the low and gradual learning curve has made Python an exceptional tool. Python cheat sheet will surely be very helpful especially for the beginners. It can be as a reference guide for easy learning and to implement Python. The sheet covers variables, data types, strings, lists to understand the Python, Numpy.  

There are a lot more data frames available but what a Pandas can offer is incredible and exceptional as well. If we see, the file storage format like PyTables and HDF5 format; and the different statistical analysis are so effective and efficient. Though Python is a readable language still once you get used to it, you won’t leave.

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