We Keep Coding

Calling mean() Function Without Removing Non-Numeric Columns In Dataframe

I have the following dataframe:
import pandas as pd
fertilityRates = pd.read_csv('fertility_rate.csv')
fertilityRatesRowCount = len(fertilityRates.axes[0])
fertilityRates.head(fertilityRatesRowCount)
I have found a way to find the mean for each row over columns 1960-1969, but would like to do so without removing the column called "Country".
The following is what is outputted after I execute the following commands:
Mean1960To1970 = fertilityRates.iloc[:, 1:11].mean(axis=1)
Mean1960To1970

You can use pandas.DataFrame.loc to select a range of years (e.g "1960":"1968" means from 1960 to 1968).
Try this :
Mean1960To1968 = (
fertilityRates[["Country"]]
.assign(Mean= fertilityRates.loc[:, "1960":"1968"].mean(axis=1))
)
# Output :
print(Mean1960To1968)
Country Mean
0 _World 5.004444
1 Afghanistan 7.450000
2 Albania 5.913333
3 Algeria 7.635556
4 Angola 7.030000
5 Antigua and Barbuda 4.223333
6 Arab World 7.023333
7 Argentina 3.073333
8 Armenia 4.133333
9 Aruba 4.044444
10 Australia 3.167778
11 Austria 2.715556

Plotly choropleth can't read State iso codes

I want to visualize the number of crimes by state using plotly express.
This is the code :
import plotly.express as px
fig = px.choropleth(grouped, locations="Code",
color="Incident",
hover_name="Code",
animation_frame='Year',
scope='usa')
fig.show()
The dataframe itself looks like this:
I only get blank map:
What is the wrong with the code?

The reason for the lack of color coding is that the United States is not specified in the location mode. please find attached a graph with locationmode='USA-states' added. You can find an example in the references. The data was created for your data.
df.head()
Year Code State incident
0 1980 AL Alabama 1445
1 1980 AK Alaska 970
2 1980 AZ Arizona 3092
3 1980 AR Arkansas 1557
4 1980 CA California 1614
import plotly.express as px
fig = px.choropleth(grouped,
locations='Code',
locationmode='USA-states',
color='incident',
hover_name="Code",
animation_frame='Year',
scope="usa")
fig.show()

How do I make a plot like the one given below with df.plot function?

I've this data of 2007 with population in Millions,GDP in Billions and index column is Country
continent year lifeExpectancy population gdpPerCapita GDP Billions
country
China Asia 2007 72.961 1318.6831 4959.11485 6539.50093
India Asia 2007 64.698 1110.39633 2452.21041 2722.92544
United States Americas 2007 78.242 301.139947 42951.6531 12934.4585
Indonesia Asia 2007 70.65 223.547 3540.65156 791.502035
Brazil Americas 2007 72.39 190.010647 9065.80083 1722.59868
Pakistan Asia 2007 65.483 169.270617 2605.94758 441.110355
Bangladesh Asia 2007 64.062 150.448339 1391.25379 209.311822
Nigeria Africa 2007 46.859 135.031164 2013.97731 271.9497
Japan Asia 2007 82.603 127.467972 31656.0681 4035.1348
Mexico Americas 2007 76.195 108.700891 11977.575 1301.97307
I am trying to plot a histogram as the following:
This was plotted using matplotlib (code below), and I want to get this with df.plot method.
The code for plotting with matplotlib:
x = data.plot(y=[3],kind = "bar")
data.plot(y = [3,5],kind = "bar",secondary_y = True,ax = ax,style='g:', figsize = (24, 6))
plt.show()

You could use df.plot() with the y axis columns you need in your plot and secondary_y argument as the second column
data[['population','gdpPerCapita']].plot(kind='bar', secondary_y='gdpPerCapita')
If you want to set the y labels for each side, then you have to get all the axes of the plot (in this case 2 y axis) and set the labels respectively.
ax1, ax2 = plt.gcf().get_axes()
ax1.set_ylabel('Population')
ax2.set_ylabel('GDP')
Output:

Plot the hist using matplotlib

Below is the dataframe
Quantity UnitPrice CustomerID
Country
Netherlands 200128 6492.55 34190538.0
EIRE 142637 48447.19 110391745.0
Germany 117448 37666.00 120075093.0
France 110480 43031.99 107648864.0
Australia 83653 4054.75 15693002.0
How to plot a histogram with condition x axis as country(rotate 90) and Quanity on Y axis
df.hist(x,y)

You can try with this:
df.plot.bar(y='Quantity')
Here's the output:

Filter and drop rows by proportion python

I have a dataframe called wine that contains a bunch of rows I need to drop.
How do i drop all rows in column 'country' that are less than 1% of the whole?
Here are the proportions:
#proportion of wine countries in the data set
wine.country.value_counts() / len(wine.country)
US 0.382384
France 0.153514
Italy 0.100118
Spain 0.070780
Portugal 0.062186
Chile 0.056742
Argentina 0.042835
Austria 0.034767
Germany 0.028928
Australia 0.021434
South Africa 0.010233
New Zealand 0.009069
Israel 0.006133
Greece 0.004493
Canada 0.002526
Hungary 0.001755
Romania 0.001558
...
I got lazy and didn't include all of the results, but i think you catch my drift. I need to drop all rows with proportions less than .01
Here is the head of my dataframe:
country designation points price province taster_name variety year price_category
Portugal Avidagos 87 15.0 Douro Roger Voss Portuguese Red 2011.0 low

You can use something like this:
df = df[df.proportion >= .01]
From that dataset it should give you something like this:
US 0.382384
France 0.153514
Italy 0.100118
Spain 0.070780
Portugal 0.062186
Chile 0.056742
Argentina 0.042835
Austria 0.034767
Germany 0.028928
Australia 0.021434
South Africa 0.010233

figured it out
country_filter = wine.country.value_counts(normalize=True) > 0.01
country_index = country_filter[country_filter.values == True].index
wine = wine[wine.country.isin(list(country_index))]