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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to check if any value is NaN in a Pandas DataFrame?
To check if any value is NaN in a Pandas DataFrame, we can use isnull().values.any() method.StepsMake a series, s, one-dimensional ndarray with axis labels (including time series).Print the series, s.Check whether NaN is present or not.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Check whether NaN is present or not.Exampleimport pandas as pd import numpy as np s = pd.Series([1, np.nan, 3, np.nan, 3, np.nan, 7, np.nan, 3]) print "Input series is:", s present = s.isnull().values.any() print "NAN is present in series: ", present df = pd.DataFrame( { "x": [5, np.nan, ...
Read MoreCreate a DataFrame with customized index parameters in Pandas
To create a DataFrame with some index, we can pass a list of values and assign them into index in DataFrame Class.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Put a list of indices in the index of DataFrame class.Print the DataFrame with the customized index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Input DataFrame is:", df df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] }, index=["John", "Jacob", "Ally", "Simon"] ) print "With Customized Index: ", dfOutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 With Customized Index: x y z John 5 4 4 Jacob 2 1 1 Ally 1 5 5 Simon 9 10 0
Read MoreHow to check if a column exists in Pandas?
To check if a column exists in a Pandas DataFrame, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a col variable with column name.Create a user-defined function check() to check if a column exists in the DataFrame.Call check() method with valid column name.Call check() method with invalid column name.Exampleimport pandas as pd def check(col): if col in df: print "Column", col, "exists in the DataFrame." else: print "Column", col, "does not exist in the DataFrame." df = pd.DataFrame( ...
Read MoreCount the frequency of a value in a DataFrame column in Pandas
To count the frequency of a value in a DataFrame column in Pandas, we can use df.groupby(column name).size() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Print frequency of column, x.Print frequency of column, y.Print frequency of column, z.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 5], "y": [4, 10, 5, 10], "z": [1, 1, 5, 1] } ) print "Input DataFrame is:", df col = "x" count = df.groupby('x').size() print "Frequency of values in column ", col, "is:", ...
Read MoreHow to use the apply() function for a single column in Pandas?
We can use apply() function on a column of a DataFrame with lambda expression.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print input DataFrame, df.Override column x with lambda x: x*2 expression using apply() method.Print the modified DataFrame.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 5], "y": [4, 10, 5, 10], "z": [1, 1, 5, 1] } ) print "Input DataFrame is:", df df['x'] = df['x'].apply(lambda x: x * 2) print "After applying multiplication of 2 DataFrame is:", dfOutputInput DataFrame is: x y z 0 5 4 1 1 2 10 1 2 1 5 5 3 5 10 1 After applying multiplication of 2 DataFrame is: x y z 0 10 4 1 1 4 10 1 2 2 5 5 3 10 10 1
Read MoreHow to sort multiple columns of a Pandas DataFrame?
To sort multiple columns of a Pandas DataFrame, we can use the sort_values() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col to sort the column.Print the sorted DataFrame.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print "Input DataFrame is:", df col = ["x", "y"] df = df.sort_values(col, ascending=[False, True]) print "After sorting column ", col, "DataFrame is:", dfOutputInput DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 After sorting column ['x', 'y'] DataFrame is: x y z 2 7 5 5 0 5 4 9 1 2 7 3 3 0 1 1
Read MoreHow to create stacked bar chart using ggvis in R?
To create stacked bar chart using ggvis, we can follow the below steps −First of all, create a data frame.Create the stacked bar chart with layer_bars function of ggvis package.Create the data frameLet's create a data frame as shown below −Group
Read MoreHow to create scatterplot for categories with grey color palette using ggplot2 in R?
To create scatterplot for categories with grey color palette using ggplot2, we can follow the below steps −First of all, create a data frame.Then, create the scatterplot for categories with default color of points.Create the scatterplot for categories with color of points in grey palette.Create the data frameLet's create a data frame as shown below −x
Read MoreHow to create boxplot for categories with grey color palette using ggplot2 in R?
To create boxplot for categories with grey color palette using ggplot2, we can follow the below steps −First of all, create a data frame.Then, create the boxplot for categories with default color of bars.Create the boxplot for categories with color of bars in grey palette.Create the data frameLet's create a data frame as shown below −Group
Read MoreHow to subset a named vector based on names in R?
To subset a named vector based on names, we can follow the below steps −Create a named vector.Subset the vector using grepl.Create the named vectorLet’s create a name vector as shown below −V
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