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In this lecture, we will learn how to import a word, name cities fine.

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In them

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for this group audience.

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We will be importing Bailey to tell female, but that's it.

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And Biton, this dataset is publicly available on trade forms like Gagen.

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And you can also download the CSP file from the resources section of this lecture.

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This dataset have two columns.

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First column is for dates and second column.

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As for the number of.

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But now let's import this database.

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We will lose Bandas reads GSV Method.

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So let's just first import Banda's and then we are importing this dataset using not greed's.

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Yes we method since this CSU file is available in the working directory off my Python library.

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I am not providing the complete but I'm just providing the file name if you want.

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You can also provide the whole part of this file instead of just the file name.

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And then my file have had.

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That's that's why I'm writing headers equate to zero zero means that heads are available at the 08 index.

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That is the first rule of my dataset.

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If you had file does not contain any headers, you have to write headers equate to none known.

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Let's just import this dataset and we are saving this dataset into an object called data frame.

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So we are creating a data frame object of Ponder's with the name Duty-Free.

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Let's just run it.

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Now to look at the first five records of this newly created.

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We can just call head matter.

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We right to frame.

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And you can see we have the first five rows of data set.

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The first column is of dates.

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The second column is our birds.

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And we have index from zero and so on.

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Now, let's check the database of this data column.

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We want to know whether PANDAS is identifying this text as a string or as a date.

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For that, we can write Dufrene.

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Then they'd call up.

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And then in more detail,

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you can see the output we are getting is awe or sense for object or in other word strings.

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So currently one does is not identifying this big text as a date.

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It is identifying this value as a string for what?

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Future data analysis.

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We want this value as dates, not as a string to do that.

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We can use a parameter that is past date using this parameter.

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We can mention the column which contains dates, and Banda's will automatically identify the format

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of that column and convert it into a debate format.

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So let's create another dappling.

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I'm calling is D.F. to.

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And simply, we are writing the same statement, but this time we are using additional parameter of

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pass state and then we are mentioning that at all to zero through.

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That is the fourth straw.

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There are dates in that column.

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So suppose if my dates are in second column, I have to write one here.

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I have to mention the index which contains the dates.

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Let us run this.

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So my beef to deter fame is ready.

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Know now to take the first fibros I can ride the f blue dot peg.

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You can see the output is similar, but this thing, this dates are in the form of the time for.

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Not in the form of the string.

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For me to check that, I can just straight up frame the do.

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Then I want to look at the date, that type of date column and just play the detailed.

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You can see earlier the fundamentals of the tape or which is sense for string.

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This time the phone my boys Emmett Emmett stands for.

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They paint for me.

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Now, you can see that does has automatically identified the format of these dates.

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So here the four on my days.

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Why, why?

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Why, why m-m didi.

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And Fondas has automatically identified that me for me and of that, if the format was of MMD Levi Way.

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In that case also pandas would have automatically identified in the format.

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But in some cases, pandas have some trouble in identifying the format.

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In those cases, you can use DEET password.

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But I think that as well.

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So only in cases where you are finding problems in loading data and loading the time data using this

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method on the in those cases you have to use date parser for date parser.

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You have to mention the format of fear data that is present in columns.

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So, for example, in this case, my data is available in the form of why year, month and days?

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Then I have to mention the format of such dates.

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For example, here, this percentage by a percentage and percentage B means that if my date is nineteen

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ninety nine slash well slash zero one, then we have a space for our.

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Then there is a colon.

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Then there is a minute and then there is a second.

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So if my date is in the form of this index affirmation the format in this way.

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So for date poza you have to first create a date parser function where you have to mention the format

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and then pass this function as an argument for date underscore parser parameter.

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You can also look at the link that I have provided you to look at all the directives that are available

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with this date parser.

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So suppose if your months are in this format, you have to write percentage B instead of percentage

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M.

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So just look at all this tapes.

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And create your own big parcel function.

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And then use it to import your dates and date time for me.

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But for most cases, you are not going to need it.

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Fondas will automatically identify the date time formic for you.

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Now, another way to import daytime data is by using cities instead of data fames.

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So here we were creating data frames.

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Our indexes were in the form of numerical data.

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But there is one more matter to import time cities where we will have dates or date time values as our

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index.

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And then a single column for the value of their cities.

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So, for example, let's just import this same data as the cities as well.

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So I am creating another object that we are calling cities.

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And here also I am using the same method be read out routes CSP.

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Then we have to mention the finally then headers and then we are using pastorate equate to zero since

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our first column contains dates.

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And then for cities, we need to make our date time data as our indexed column.

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So I am writing index underscore call equate to zero.

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That is our first column is the index for our cities.

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And then to convert this data frame in two cities, I can read it squeeze equate to grow just on this.

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So my series is really.

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Let's look at the first five aloose.

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You can see these are the first five values.

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You can notice now the city's name is, but there is no individual column name.

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The values that are contained in this series is OK.

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But so instead of column name, we have our city's name as Bert's.

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The big type is in digits since this second column contains integer values.

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And for this series, we have big time values as our indexes.

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And this second column that is the column is the values of our cities.

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So the on the difference between data frame and cities is that in data frame we have numerical indexes

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from zero to the land of ordinary Dufrene.

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We have two columns.

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First one is dead.

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And the second one is birth in cities.

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We have indexes as a World Time Series data and the values corresponding to those indexes as the values

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of cities.

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Now let's look at some different attributes of their data frame.

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And the cities that we have created.

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To look at the size of Ford data from cities, we can right dot shaped my head.

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So c.D or Chip?

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Sidis is our object name here.

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We are just using dot ship attribute.

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Similarly, what do Dufferin words D.F. do?

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Using dot ship.

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You can see we have it on 365 values in our cities.

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Since cities only have one single column.

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You cannot have more than one column in your cities.

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Let's say we are not getting the number of columns.

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Whereas for the data frame that we have created, we have two columns, first one for date and second

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one for the birth.

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That's why we are getting 365 Pommer too.

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There are 365 rows and two columns.

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Now let's look at how to get that subset of our cities or our data frame.

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So far, cities, if I want values of January of 1959, I can just ride cities and I can mention the

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year and the month.

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Well, you remember, this is the advantage of using the time format and sort of a string for me here.

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I'm not mentioning the days value.

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I will automatically get all the values of January 1959.

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If you're on this, you can see that I'm getting all the values from day one to day 31.

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This is the way to slice your data.

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In case of cities, if you just write 1959 here, you will get data for all the months and all the days.

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Similarly for data frame, we can write condition.

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So here we want the values from the data frame.

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We're the data frame date.

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Value is more than this.

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And data frame data value is less than this.

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So for this 21 days from 2nd Gen to 21st Gen.

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If I want to get a value, I can write like this.

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Here also, this is the advantage of using date time for me.

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If we were using string format, this was just a string for Bandas and it will not be able to identify

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whether January two is between these two values or not.

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But since we are using data format, Banda's can identify that Denbury five lies between first gen and

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21st Gen.

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Let's run this as well.

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You can see the result is as expected.

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We have values from 2nd Gen build 21st Gen.

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So in case of series you can just write them on values and you will get all the values.

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But for the top frame, you have to mention the range of dates between which you want the data now to

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find important statistics about your data frame or series.

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You can use Don describe method.

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So just write your series or data frame, name and use or describe method here.

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You will get values of the number of homes, mean standard deviation, minimum value, the value present

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at the 25th percentile of the value presented.

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The 15th percentile and the value presented in the fifth percentile.

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And the maximum value of your cities.

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So for our cities, the total number of observations are 365.

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The mean of all these values is forty one point nine eight.

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Standard deviation is seven point three.

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The minimum values frantically, the maximum value is 73.

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And then we have that 25 feet and 75 percent value as well.

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The name of this series is Births and the D type is flawed.

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Similarly, this is the stats for our cities for data frame can again use Dort describe method here.

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Also, you will get all these values for your columns which contain numerical value.

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So the statistics are the same since we have loaded the same data.

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So this is the way to load your data and to get some basic statistics about your data.

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Now, to summarize, you can load your data as a data frame.

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And in case, if you have only one column for euro value, you can also imported as your cities four

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dates.

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You need to take special care to import it in our day time format.

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Using part state and mentioning the column name for Seabees and data frame containing time series data,

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you can use all the basic methods of pandas data Dufrene such as head for looking at the first five

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values shape for looking at the size of your data frame and or describe metric to look at some basic

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statistics about your affair.

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That's all for this video.

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Thank you.