Develop Reference

Iterating Conditions through Pandas .loc

I just wanted to ask the community and see if there is a more efficient to do this.
I have several rows in a data frame and I am using .loc to filter values in row A for I can perform calculations on row B.
I can easily do something like...
filter_1 = df.loc['Condition'] = 1
And then perform the mathematical calculation on row B that I need.
But there are many conditions I must go through so I was wondering if I could possibly make a list of the conditions and then iterate them through the .loc function in less lines of code?
Would something like this work where I create a list, then iterate the conditions through a loop?
Thank you!
This example gets most of what I want. I just need it to show 6.4 and 7.0 in this example. How can I manipulate the iteration for it shows the results for the unique values in row 'a'?
import pandas as pd
a = [1,2,1,2,1,2,1,2,1,2]
b = [5,1,3,5,7,20,9,5,8,4]
col = ['a', 'b']
list_1 = []
for i, j in zip(a,b):
list_1.append([i,j])
df1 = pd.DataFrame(list_1, columns= col)
for i in a:
aa = df1[df1['a'].isin([i])]
aa1 = aa['b'].mean()
print (aa1)

Solution using set
set_a = set(a)
for i in set_a:
aa = df[df['a'].isin([i])]
aa1 = aa['b'].mean()
print (aa1)
Solution using pandas mean function
Is this what you are looking for?
import pandas as pd
a = [1,2,1,2,1,2,1,2,1,2]
b = [5,1,3,5,7,20,9,5,8,4]
df = pd.DataFrame({'a':a,'b':b})
print (df)
print(df.groupby('a').mean())
The results from this are:
Original Dataframe df:
a b
0 1 5
1 2 1
2 1 3
3 2 5
4 1 7
5 2 20
6 1 9
7 2 5
8 1 8
9 2 4
The mean value of df['a'] is:
b
a
1 6.4
2 7.0

Here you go:
df = df[(df['A'] > 1) & (df['A'] < 10)]

Pivot table to "tidy" data frame in Pandas

I have an array of numbers (I think the format makes it a pivot table) that I want to turn into a "tidy" data frame. For example, I start with variable 1 down the left, variable 2 across the top, and the value of interest in the middle, something like this:
X Y
A 1 2
B 3 4
I want to turn that into a tidy data frame like this:
V1 V2 value
A X 1
A Y 2
B X 3
B Y 4
The row and column order don't matter to me, so the following is totally acceptable:
value V1 V2
2 A Y
4 B Y
3 B X
1 A X
For my first go at this, which was able to get me the correct final answer, I looped over the rows and columns. This was terribly slow, and I suspected that some machinery in Pandas would make it go faster.
It seems that melt is close to the magic I seek, but it doesn't get me all the way there. That first array turns into this:
V2 value
0 X 1
1 X 2
2 Y 3
3 Y 4
It gets rid of my V1 variable!
Nothing is special about melt, so I will be happy to read answers that use other approaches, particularly if melt is not much faster than my nested loops and another solution is. Nonetheless, how can I go from that array to the kind of tidy data frame I want as the output?
Example dataframe:
df = pd.DataFrame({"X":[1,3], "Y":[2,4]},index=["A","B"])

Use DataFrame.reset_index with DataFrame.rename_axis and then DataFrame.melt. If you want order columns we could use DataFrame.reindex.
new_df = (df.rename_axis(index = 'V1')
.reset_index()
.melt('V1',var_name='V2')
.reindex(columns = ['value','V1','V2']))
print(new_df)
Another approach DataFrame.stack:
new_df = (df.stack()
.rename_axis(index = ['V1','V2'])
.rename('value')
.reset_index()
.reindex(columns = ['value','V1','V2']))
print(new_df)
value V1 V2
0 1 A X
1 3 B X
2 2 A Y
3 4 B Y
to names names there is another alternative like commenting #Scott Boston in the comments

Melt is a good approach, but it doesn't seem to play nicely with identifying the results by index. You can reset the index first to move it to its own column, then use that column as the id col.
test = pd.DataFrame([[1,2],[3,4]], columns=['X', 'Y'], index=['A', 'B'])
X Y
A 1 2
B 3 4
test = test.reset_index()
index X Y
0 A 1 2
1 B 3 4
test.melt('index',['X', 'Y'], 'prev cols')
index prev cols value
0 A X 1
1 B X 3
2 A Y 2
3 B Y 4

Python iterate thru csv and execute point in polygon operation

My goal is to iterate thru data frame from CSV file and perform a point in polygon operation and assign this to a new column in the data frame. I was able to construct function and my only problem is how to iterate thru data frame and append with results of the function.
from shapely.geometry import Point, shape
point = Point(10.2023019,8.43254802)
fc = fiona.open("ngaadmbndaadm2osgof20170222.geojson")
print (fc.schema)
and then print value for values from the point:
for feature in fc:
if shape(feature['geometry']).contains(point):
df_data['Admin2name'] = (feature['properties']['admin2Name'])
My data frame looks like this

It is usually not efficient to iterate through the dataframe. Instead you may want to read your csv file to a different dataframe and merge with your current dataframe on the X&Y coordinates columns.

I found a solution with another method executing spatial join (merge) on two geodataframes
merged_gdf = gpd.sjoin(gdf, df_lga, how="inner", op="within")
For others who will have this problem full code below
import pandas as pd
import geopandas as gpd
df_lga = gpd.read_file('ngaadmbndaadm2osgof20170222.geojson') #first df
df_data = pd.read_csv('nigeria_healthfacilities.csv', low_memory=False)# second df
geometry = [Point(xy) for xy in zip(df_data.X, df_data.Y)] # transpose lat, long to shapely geomentry point
crs = {'init': 'epsg:4326'} #set crs
gdf = GeoDataFrame(df, crs=crs, geometry=geometry)
merged_gdf = gpd.sjoin(gdf, df_lga, how="inner", op="within")# merge two df into one that spatialy joins points in polygons
merged_gdf

As far as I can tell, your question isn't very clear but I assume you're asking how to create a new column as a function of previous columns? You almost never want to iterate through all columns but if the Dataframe is small enough it won't matter of course.
You'll want to use vectorization if possible:
df = pd.DataFrame({'A' : [1,2,3,4,5,6], 'B': [6,5,4,3,2,1]})
df
>>> A B
0 1 6
1 2 5
2 3 4
3 4 3
4 5 2
5 6 1
Now you can simple define a new column directly from A and B:
df['C'] = (df['A']*df['B'] >= 8)
df
>>> A B C
0 1 6 False
1 2 5 True
2 3 4 True
3 4 3 True
4 5 2 True
5 6 1 False
Or alternatively, use apply to apply a function:
df['D'] = df['B'].apply(lambda x: -x)
df
>>> A B C D
0 1 6 False -6
1 2 5 True -5
2 3 4 True -4
3 4 3 True -3
4 5 2 True -2
5 6 1 False -1

How to delete all columns in DataFrame except certain ones?

Let's say I have a DataFrame that looks like this:
a b c d e f g
1 2 3 4 5 6 7
4 3 7 1 6 9 4
8 9 0 2 4 2 1
How would I go about deleting every column besides a and b?
This would result in:
a b
1 2
4 3
8 9
I would like a way to delete these using a simple line of code that says, delete all columns besides a and b, because let's say hypothetically I have 1000 columns of data.
Thank you.

In [48]: df.drop(df.columns.difference(['a','b']), 1, inplace=True)
Out[48]:
a b
0 1 2
1 4 3
2 8 9
or:
In [55]: df = df.loc[:, df.columns.intersection(['a','b'])]
In [56]: df
Out[56]:
a b
0 1 2
1 4 3
2 8 9
PS please be aware that the most idiomatic Pandas way to do that was already proposed by #Wen:
df = df[['a','b']]
or
df = df.loc[:, ['a','b']]

Another option to add to the mix. I prefer this approach for readability.
df = df.filter(['a', 'b'])
Where the first positional argument is items=[]
Bonus
You can also use a like argument or regex to filter.
Helpful if you have a set of columns like ['a_1','a_2','b_1','b_2']
You can do
df = df.filter(like='b_')
and end up with ['b_1','b_2']
Pandas documentation for filter.

there are multiple solution .
df = df[['a','b']] #1
df = df[list('ab')] #2
df = df.loc[:,df.columns.isin(['a','b'])] #3
df = pd.DataFrame(data=df.eval('a,b').T,columns=['a','b']) #4 PS:I do not recommend this method , but still a way to achieve this

Hey what you are looking for is:
df = df[["a","b"]]
You will recive a dataframe which only contains the columns a and b

If you only want to keep more columns than you're dropping put a "~" before the .isin statement to select every column except the ones you want:
df = df.loc[:, ~df.columns.isin(['a','b'])]

If you have more than two columns that you want to drop, let's say 20 or 30, you can use lists as well. Make sure that you also specify the axis value.
drop_list = ["a","b"]
df = df.drop(df.columns.difference(drop_list), axis=1)

How to edit/add two columns to a dataframe in pandas at once - df.apply()

So I've been doing things like this with pandas:
usrdata['columnA'] = usrdata.apply(functionA, axis=1)
in order to do row operations and changing/adding columns to my dataframe.
However, now I want to try to do something like this:
usrdata['columnB', 'columnC'] = usrdata.apply(functionB, axis=1)
But the output of function B is a Series with only one column in a tuple (with two values for each row) apparently. Is there a nice way for me to either:
format the output from functionB so it can readily be added to my
dataframe
add (and possibly have to unpack) the output from functionB and assign each each column to each column of my dataframe?

Try using zip:
usrdata['columnB'], usrdata['columnC'] = zip(*usrdata.apply(functionB, axis=1))

I'd assign directly to a df consisting of your new df's and modify the func body to return a Series constructed with a list of the data:
In [9]:
df = pd.DataFrame({'a':[1, 2, 3, 4, 5]})
df
Out[9]:
a
0 1
1 2
2 3
3 4
4 5
In [10]:
def func(x):
return pd.Series([x*3, x*10])
​
df[['b','c']] = df['a'].apply(func)
df
Out[10]:
a b c
0 1 3 10
1 2 6 20
2 3 9 30
3 4 12 40
4 5 15 50