I have a dataset of U.S. Education Datasets: Unification Project. I want to find out
I am having problem in updating the count whenever the value in the if statement is correct.
import pandas as pd
import numpy as np
df = pd.read_csv("C:/Users/akash/Downloads/states_all.csv")
df.shape
df = df.iloc[:, -6]
for key, value in df.iteritems():
count = 0
count1 = 0
if value < 5000:
count += 1
elif value < 20000 and value > 10000:
count1 += 1
print(str(count) + str(count1))
df looks like this
0 196386.0
1 30847.0
2 175210.0
3 123113.0
4 1372011.0
5 160299.0
6 126917.0
7 28338.0
8 18173.0
9 511557.0
10 315539.0
11 43882.0
12 66541.0
13 495562.0
14 278161.0
15 138907.0
16 120960.0
17 181786.0
18 196891.0
19 59289.0
20 189795.0
21 230299.0
22 419351.0
23 224426.0
24 129554.0
25 235437.0
26 44449.0
27 79975.0
28 57605.0
29 47999.0
...
1462 NaN
1463 NaN
1464 NaN
1465 NaN
1466 NaN
1467 NaN
1468 NaN
1469 NaN
1470 NaN
1471 NaN
1472 NaN
1473 NaN
1474 NaN
1475 NaN
1476 NaN
1477 NaN
1478 NaN
1479 NaN
1480 NaN
1481 NaN
1482 NaN
1483 NaN
1484 NaN
1485 NaN
1486 NaN
1487 NaN
1488 NaN
1489 NaN
1490 NaN
1491 NaN
Name: GRADES_9_12_G, Length: 1492, dtype: float64
In the output I got
00
I downloaded your data set, and there are multiple ways to go about this. First of all, you do not need to subset your data if you do not want to. Your problem can be solved like this:
import pandas as pd
df = pd.read_csv('states_all.csv')
df.fillna(0, inplace=True) # fill NA with 0, not required but nice looking
print(len(df.loc[df['GRADES_9_12_G'] < 5000])) # 184
print(len(df.loc[(df['GRADES_9_12_G'] > 10000) & (df['GRADES_9_12_G'] < 20000)])) # 52
The line df.loc[df['GRADES_9_12_G'] < 5000] is telling pandas to query the dataframe for all rows in column df['GRADES_9_12_G'] that are less than 5000. I am then calling python's builtin len function to return the length of the returned, which outputs 184. This is essentially a boolean masking process which returns all True values for your df that meet the conditions you give it.
The second query df.loc[(df['GRADES_9_12_G'] > 10000) & (df['GRADES_9_12_G'] < 20000)]
uses an & operator which is a bitwise operator that requires both conditions to be met for a row to be returned. We then call the len function on that as well to get an integer value of the number of rows which outputs 52.
To go off your method:
import pandas as pd
df = pd.read_csv('states_all.csv')
df.fillna(0, inplace=True) # fill NA with 0, not required but nice looking
df = df.iloc[:, -6] # select all rows for your column -6
print(len(df[df < 5000])) # query your "df" for all values less than 5k and print len
print(len(df[(df > 10000) & (df < 20000)])) # same as above, just for vals in between range
Why did I change the code in my answer instead of using yours?
Simply enough to say, it is more pandonic. Where we can, it is cleaner to use pandas built-ins than iterating over dataframes with for loops, as this is what pandas was designed for.