This question already has answers here:
How do I operate on a DataFrame with a Series for every column?
(3 answers)
What does the term "broadcasting" mean in Pandas documentation?
(2 answers)
Closed 2 years ago.
I've come across something that while looking at how to normalize data on datacamp.com
In one of the exercises it was said that to normalize a dataframe df one should do like this
normalized_df = df / df.mean()
My question is: why does this work? Why does Pandas know here to divide columnwise?
Thanks
Edit: This post Dataframe divide series on pandas does not answer the question I am posting. I merely states what to do. I want to know why it works.
Related
This question already has answers here:
Boxplot of Multiple Columns of a Pandas Dataframe on the Same Figure (seaborn)
(4 answers)
Closed 24 days ago.
I'm a beginner trying to learn data science and this is my first time using the seaborn and matplotlib libraries. I have this practice dataset and data frame :
that I want to turn into a box plot and I want the x-axis to have all of the column names and the y-axis to range from 0 - 700 but, I'm not sure what to do.
I tried using : random_variable = sms.boxplot(data = df, x = ?, y = 'TAX')
which does give me the y-axis that is close to what I am looking for but, I don't know what the x-axis should be set equal too.
I thought may I could use the keys of the dataframe but, all I got was this mess that doesn't work:
random_variable = sms.boxplot(x = df.keys(), y = df['TAX'])
I want it to look like this but, I'm really lost on how to do this:
I apologize if this is an easy fix but, I would appreciate any help.
If you just want to display your data like that go with
import seaborn as sns
sns.boxplot(data=df)
This question already has answers here:
How to change the number or rows and columns in my catplot
(2 answers)
Seaborn multiple barplots
(2 answers)
subplotting with catplot
(1 answer)
Closed 4 months ago.
I have this dataset table
And i want to plot profit made by different sub_category in different region.
now i am using this code to make a plot using seaborn
sns.barplot(data=sub_category_profit,x="sub_category",y="profit",hue="region")
I am getting a extreamly huge plot like this output
is there is any way i can get sub-plots of this like a facet-gird. Like subplots of different sub_category. I have used the facet grid function but it is the also not working properly.
g=sns.FacetGrid(data=sub_category_profit,col="sub_category")
g.map(sns.barplot(data=sub_category_profit,x="region",y="profit"))
I am getting the following output
As you can see in the facet grid output the plots are very small and the bar graph is just present on one grid.
See docs on seaborn.FacetGrid, particularly the posted example, where you should not pass the data again in the map call but simply the plot function and x and y variables to draw plots to corresponding facets.
Also, consider the col_wrap argument since you do not specify row to avoid the very wide plot output.
g=sns.FacetGrid(data=sub_category_profit, col="sub_category", col_wrap=4)
g.map_dataframe(sns.barplot, x="region", y="profit")
This question already has answers here:
Plot a horizontal line on a given plot
(7 answers)
Closed 1 year ago.
I want to draw a line on y=0. However, I am not sure how to do that using pandas built-in charting.
df.mean(axis=1).plot() I am running this and that is the result.
Catch the ax instance returned by plot, and you can use axhline:
ax = df.mean(axis=1).plot()
ax.axhline(0, c='r')
Output:
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pandas pd.options.display.max_rows not working as expected
(2 answers)
Pandas: Setting no. of max rows
(10 answers)
Closed 1 year ago.
rather than having the ....(dot dot) view when opening a data frame, how to access or see all the values in Jupyter notebook. reference image
You can set "display.max_rows" option in pandas: pd.set_option('display.max_rows', None) or pd.set_option('display.max_rows', LARGE_NUMBER)
To set it temporarily use a context manager:
with pd.option_context('display.max_rows', None):
print(df)
This question already has answers here:
How to convert a Scikit-learn dataset to a Pandas dataset
(27 answers)
Closed 2 years ago.
I am trying to convert this Python code section to pandas dataframe:
iris = datasets.load_iris()
x = iris.data
y = iris.target
I will like to import Iris data on my local machine instead of loading the data from Scikit library. Your kind suggestions would be highly appreciated.
Use from_records and concat to create a datafram. then rename the columns:
df = pd.concat([pd.DataFrame.from_records(x),pd.DataFrame(y)],axis=1)
df.columns = ['col1','col2','col3','col4','target']