Performing Analysis of Meteorological Data

Observational Meteorological Data — Data consisting of physical parameters that are measured directly by instrumentation, and include temperature, dew point, wind direction, wind speed, cloud cover, cloud layer(s), ceiling height, visibility, current weather, and precipitation amount.

Use Weather data which is easily available on Kaggle or on google. choose data in which all type of weather condition are taken .

Use Panda library for these operation.

Use following function

nunique() is used to get a count of unique values. To download the CSV file used, Click Here. Return Type: Integer — Number of unique values in a column. In this example, nunique() method is used to get number of all unique values in Team column

The value_counts() function is used to get a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default

filter() function is used to Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

groupby() function is used to split the data into groups based on some criteria. pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names.

isnull() function detects the missing value of an object and the DataFrame. notnull() function detects the non-missing value of an object.

Using rename() function. One way of renaming the columns in a Pandas dataframe is by using the rename() function. This method is quite useful when we need to rename some selected columns because we need to specify information only for the columns which are to be renamed. Rename a single column

The mean() function is used to return the mean of the values for the requested axis. If we apply this method on a Series object, then it returns a scalar value, which is the mean value of all the observations in the dataframe.

std() is defined as a function for calculating the standard deviation of the given set of numbers, DataFrame, column, and rows. In respect to calculate the standard deviation, we need to import the package named “statistics” for the calculation of median.

Calculate the variance of a Pandas DataFrame by using the pd. var() function that calculates the variance along all columns. You can then get the column you’re interested in after the computation.

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