00:00Hello everyone, in the video we will talk about introduction to matplotlib and c1.
00:07So first we will talk about matplotlib.
00:11So matplotlib is a visualization tool.
00:14Visualization tool.
00:17So we will use 2D graphs and we will easily analyze.
00:22So in the matplotlib we will create line chart, bar chart, histogram.
00:29Pychart, scatterplot, boxplot.
00:35This is the main support of matplotlib.
00:39Now we will use the data in the pandas library.
00:42We will use group by one specific result.
00:46So we will apply to the customizable data.
00:50So we will create flexibility in matplotlib.
00:54So we will create a new customization.
00:58That will help matplotlib support.
01:00Now we will know how many graphs we can create.
01:04That's how we can create.
01:06We will use matplotlib documentation.
01:08So we will use documentation.
01:10How to create and learn.
01:12So extensive community support and documentation.
01:15This is the advantages.
01:18Disadvantages.
01:19Disadvantages are complex.
01:21We have data very complex.
01:23Like we have code to do.
01:24So it's time consuming.
01:26This is the additional customization.
01:28This is the additional customization.
01:29This is the basic and y-axis.
01:33This is the customization.
01:35This is the additional customization.
01:36That will be separate code.
01:37Now we will use matplotlib.
01:39Now we will use step count.
01:41Step count analyze.
01:43A line chart.
01:44This is the step count.
01:45This is the step count.
01:47This week is the step count.
01:49This week is the step count.
01:51This week is the step count.
01:52This week and last week.
01:53This week is the weekly basis data.
01:55X axis the available.
01:57Weekly data.
01:58So now Thursday count look at us.
02:00This week and last week.
02:03This week count is 25000.
02:05Last week step count is 1500.
02:11This week is customizable.
02:13Customizable.
02:14We can visualize them.
02:15So that we can use matplot.
02:21Next is seebon.
02:23So, C-Bond is an advanced matplot version.
02:38In this matplot, C-Bond is created.
02:41So, we use an appealing statistical plot.
02:44This is the minimum number of quotes.
02:48We use different style plots.
02:51Now, if we do a data analysis,
02:54like independent columns and dependent columns,
02:57correlation analyze.
03:00If we analyze values,
03:02minus 1 to 1,
03:04that's how values are.
03:06Now, values are 4 columns.
03:09Diagonal elements are 1.
03:11This values are 0.5, 0.75, 0.2.
03:15This is the correlation plot analyze.
03:18Now, we use a highly color palette visualization.
03:22Heatmap.
03:24That's how we use.
03:26Next, one column is the data.
03:29The correlation value is the visualization.
03:32We use pair plot.
03:34So, that's how complex data set.
03:36We use a c-Bond to create.
03:39We use a minimal amount of code.
03:42We use a minimum amount of code.
03:44And, we use a matplot to use highly flexible.
03:46And, there is a very low flexibility.
03:48Then, we use a matplot to create different types of charts.
03:53And, we use a limited types of charts.
03:57Very defined.
03:58The CEF requirements.
03:59Okay, let's see.
04:00And, we use a matplot loop and chases.
04:03Next, if you want a matplot loop?
04:11Basically, your즈ane and numpy can integrate it into a matplot loop?
04:15Next, if you put a matplot loop, Pandas, and Numpy.
04:17You can integrate it into the matplot loop.
04:19Next, our Python song owns the support.
04:23instruction
04:43functions
04:44databases
04:46if you put in a light
04:47we can change
04:50documentation
04:52Documentation is great because we have inbuilt data set support like 25 to 30 data sets.
05:03We will result in the data set.
05:08We will use machine learning.
05:10Then we will create charts as well as superior.
05:14Specifically, we will use x-axis and y-axis.
05:19We will create x-axis and y-axis.
05:25This is the advantage.
05:27In machine learning, NumPy, Pandas, Matplotlib, C-Bone.
05:31We will integrate everything.
05:37This is Matplotlib and C-Bone.
05:39We will see you in the next video.
