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Column Selection : In Order to select a column in Pandas DataFrame, we can either access the columns by calling them by their columns name. Rows can also be selected by passing integer location to an iloc[] function. Output: As shown in the output image, two series were returned since there was only one parameter both of the times. For more Details refer to Dealing with Rows and Columns. Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame. Indexing could mean selecting all the rows and some of the columns, some of the rows and all of the columns, or some of each of the rows and columns.

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Indexing can also be known as Subset Selection. Indexing a Dataframe using indexing operator [] : Indexing operator is used to refer to the square brackets following an object. In this indexing operator to refer to df[]. The df. It can select subsets of rows or columns.

It can also simultaneously select subsets of rows and columns. In order to select a single row using. Indexing a DataFrame using. Missing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in real life scenario. Checking for missing values using isnull and notnull : In order to check missing values in Pandas DataFrame, we use a function isnull and notnull. Both function help in checking whether a value is NaN or not. These function can also be used in Pandas Series in order to find null values in a series.

All these function help in filling a null values in datasets of a DataFrame. Interpolate function is basically used to fill NA values in the dataframe but it uses various interpolation technique to fill the missing values rather than hard-coding the value. Now we drop rows with at least one Nan value Null value.

Iteration is a general term for taking each item of something, one after another. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary. Iterating over rows : In order to iterate over rows, we can use three function iteritems , iterrows , itertuples. These three function will help in iteration over rows. Now we apply iterrows function in order to get a each element of rows.

It's not immediately obvious where axis comes from and why you need it to be 1 for it to affect columns. To see why, just look at the. As we learned above, this is a tuple that represents the shape of the DataFrame, i. Note that the rows are at index zero of this tuple and columns are at index one of this tuple. This comes from NumPy, and is a great example of why learning NumPy is worth your time.

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Imputation is a conventional feature engineering technique used to keep valuable data that have null values. There may be instances where dropping every row with a null value removes too big a chunk from your dataset, so instead we can impute that null with another value, usually the mean or the median of that column. First we'll extract that column into its own variable:. If you remember back to when we created DataFrames from scratch, the keys of the dict ended up as column names.

Now when we select columns of a DataFrame, we use brackets just like if we were accessing a Python dictionary. We have now replaced all nulls in revenue with the mean of the column. Imputing an entire column with the same value like this is a basic example. It would be a better idea to try a more granular imputation by Genre or Director. For example, you would find the mean of the revenue generated in each genre individually and impute the nulls in each genre with that genre's mean.

Using describe on an entire DataFrame we can get a summary of the distribution of continuous variables:. Understanding which numbers are continuous also comes in handy when thinking about the type of plot to use to represent your data visually. By using the correlation method. Positive numbers indicate a positive correlation — one goes up the other goes up — and negative numbers represent an inverse correlation — one goes up the other goes down.

So looking in the first row, first column we see rank has a perfect correlation with itself, which is obvious. A little more interesting. Examining bivariate relationships comes in handy when you have an outcome or dependent variable in mind and would like to see the features most correlated to the increase or decrease of the outcome. You can visually represent bivariate relationships with scatterplots seen below in the plotting section.

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For a deeper look into data summarizations check out Essential Statistics for Data Science. Up until now we've focused on some basic summaries of our data. We've learned about simple column extraction using single brackets, and we imputed null values in a column using fillna.

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Below are the other methods of slicing, selecting, and extracting you'll need to use constantly. It's important to note that, although many methods are the same, DataFrames and Series have different attributes, so you'll need be sure to know which type you are working with or else you will receive attribute errors.

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This will return a Series. To extract a column as a DataFrame , you need to pass a list of column names. In our case that's just a single column:. Remember that we are still indexed by movie Title, so to use. To show this even further, let's select multiple rows. How would you do it with a list? It's works the same way in pandas:. One important distinction between using.

Slicing with.

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For example, what if we want to filter our movies DataFrame to show only films directed by Ridley Scott or films with a rating greater than or equal to 8. To do that, we take a column from the DataFrame and apply a Boolean condition to it. Here's an example of a Boolean condition:. Similar to isnull , this returns a Series of True and False values: True for films directed by Ridley Scott and False for ones not directed by him.

To return the rows where that condition is True we have to pass this operation into the DataFrame:. Let's look at conditional selections using numerical values by filtering the DataFrame by ratings:. We need to make sure to group evaluations with parentheses so Python knows how to evaluate the conditional. Let's say we want all movies that were released between and , have a rating above 8.

If you recall up when we used. It is possible to iterate over a DataFrame or Series as you would with a list, but doing so — especially on large datasets — is very slow. An efficient alternative is to apply a function to the dataset. For example, we could use a function to convert movies with an 8.

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Now we want to send the entire rating column through this function, which is what apply does:. You can also use anonymous functions as well. Overall, using apply will be much faster than iterating manually over rows because pandas is utilizing vectorization. A good example of high usage of apply is during natural language processing NLP work. You'll need to apply all sorts of text cleaning functions to strings to prepare for machine learning. Another great thing about pandas is that it integrates with Matplotlib, so you get the ability to plot directly off DataFrames and Series.