Data Handling Guidebook
The one stop shop to learn about data intake, processing, and visualization.
This homepage will show you just some of the things you can do with data.
Install
Either:
pip install your_project_name
from google.colab import drive
drive.mount('/content/drive/My Drive/colabs/test_template')
OR:
from test_template.acsDownload import retrieve_acs_data
How to use
Fill me in please! Don't forget code examples:
# Our download function will use Baltimore City's tract, county and state as internal paramters
# Change these values in the cell below using different geographic reference codes will change those parameters
tract = '*'
county = '510'
state = '24'
# Specify the download parameters the function will receieve here
tableId = 'B19001'
year = '17'
saveAcs = True
df = retrieve_acs_data(state, county, tract, tableId, year, saveAcs)
df.head()
Number of Columns 17
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
B19001_001E_Total | B19001_002E_Total_Less_than_$10_000 | B19001_003E_Total_$10_000_to_$14_999 | B19001_004E_Total_$15_000_to_$19_999 | B19001_005E_Total_$20_000_to_$24_999 | B19001_006E_Total_$25_000_to_$29_999 | B19001_007E_Total_$30_000_to_$34_999 | B19001_008E_Total_$35_000_to_$39_999 | B19001_009E_Total_$40_000_to_$44_999 | B19001_010E_Total_$45_000_to_$49_999 | B19001_011E_Total_$50_000_to_$59_999 | B19001_012E_Total_$60_000_to_$74_999 | B19001_013E_Total_$75_000_to_$99_999 | B19001_014E_Total_$100_000_to_$124_999 | B19001_015E_Total_$125_000_to_$149_999 | B19001_016E_Total_$150_000_to_$199_999 | B19001_017E_Total_$200_000_or_more | state | county | tract | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NAME | ||||||||||||||||||||
Census Tract 1901 | 796 | 237 | 76 | 85 | 38 | 79 | 43 | 36 | 35 | 15 | 43 | 45 | 39 | 5 | 0 | 6 | 14 | 24 | 510 | 190100 |
Census Tract 1902 | 695 | 63 | 87 | 93 | 6 | 58 | 30 | 14 | 29 | 23 | 38 | 113 | 70 | 6 | 32 | 11 | 22 | 24 | 510 | 190200 |
Census Tract 2201 | 2208 | 137 | 229 | 124 | 52 | 78 | 87 | 50 | 80 | 13 | 217 | 66 | 159 | 205 | 167 | 146 | 398 | 24 | 510 | 220100 |
Census Tract 2303 | 632 | 3 | 20 | 0 | 39 | 7 | 0 | 29 | 8 | 9 | 44 | 29 | 98 | 111 | 63 | 94 | 78 | 24 | 510 | 230300 |
Census Tract 2502.07 | 836 | 102 | 28 | 101 | 64 | 104 | 76 | 41 | 40 | 47 | 72 | 28 | 60 | 19 | 27 | 15 | 12 | 24 | 510 | 250207 |