Pandas 将列添加到关联字符串值的新数据帧？

Question

I am trying to add a column from one dataframe to another,

df.head()

street_map2[["PRE_DIR","ST_NAME","ST_TYPE","STREET_ID"]].head()

The PRE_DIR is just the prefix of the street name. What I want to do is add the column STREET_ID at the associated street to df . I have tried a few approaches but my inexperience with pandas and the comparison of strings is getting in the way,

street_map2['STREET'] = df["STREET"]
street_map2['STREET'] = np.where(street_map2['STREET'] == street_map2["ST_NAME"])

The above code shows an "ValueError: Length of values does not match length of index". I've also tried using street_map2['STREET'].str in street_map2["ST_NAME"].str . Can anyone think of a good way to do this? (note it doesn't need to be 100% accurate just get most and it can be completely different from the approach tried above)

EDIT Thank you to all who have tried so far I have not resolved the issues yet. Here is some more data,

street_map2["ST_NAME"]

I have tried this approach as suggested but still have some indexing problems,

def get_street_id(street_name):
     return street_map2[street_map2['ST_NAME'].isin(df["STREET"])].iloc[0].ST_NAME

df["STREET_ID"] = df["STREET"].map(get_street_id)
df["STREET_ID"]

This throws this error,

If it helps the data frames are not the same length. Any more ideas or a way to fix the above would be greatly appreciated.

Answer 1

For you to do this, you need to merge these dataframes. One way to do it is:

df.merge(street_map2, left_on='STREET', right_on='ST_NAME')

What this will do is: it will look for equal values in ST_NAME and STREET columns and fill the rows with values from the other columns from both dataframes.

Check this link for more information: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html

Also, the strings on the columns you try to merge on have to match perfectly (case included).

Answer 2

You can do something like this, with a map function:

df["STREET_ID"] = df["STREET"].map(get_street_id)

Where get_street_id is defined as a function that, given a value from df["STREET"] . will return a value to insert into the new column:

(disclaimer; currently untested)

def get_street_id(street_name):
    return street_map2[street_map2["ST_NAME"] == street_name].iloc[0].ST_NAME

We get a dataframe of street_map2 filtered by where the st-name column is the same as the street-name:

street_map2[street_map2["ST_NAME"] == street_name]

Then we take the first element of that with iloc[0] , and return the ST_NAME value.

We can then add that error-tolerance that you've addressed in your question by updating the indexing operation:

...
street_map2[street_map2["ST_NAME"].str.contains(street_name)]
...

or perhaps,

...
street_map2[street_map2["ST_NAME"].str.startswith(street_name)]
...

---

Or, more flexibly:

...
street_map2[
    street_map2["ST_NAME"].str.lower().replace("street", "st").startswith(street_name.lower().replace("street", "st"))
]
...

...which will lowercase both values, convert, for example, "street" to "st" (so the mapping is more likely to overlap) and then check for equality.

If this is still not working for you, you may unfortunately need to come up with a more accurate mapping dataset between your street names. It is very possible that the street names are just too different to easily match with string comparisons.

(If you're able to provide some examples of street names and where they should overlap, we may be able to help you better develop a "fuzzy" match!)

Answer 3

Alright, I managed to figure it out but the solution probably won't be too helpful if you aren't in the exact same situation with the same data. Bernardo Alencar's answer was essential correct except I was unable to apply an operation on the strings while doing the merge (I still am not sure if there is a way to do it). I found another dataset that had the street names formatted similar to the first. I then merged the first with the third new data frame. After this I had the first and second both with columns ["STREET_ID"] . Then I finally managed to merge the second one with the combined one by using,

temp = combined["STREET_ID"]
CrimesToMapDF = street_maps.merge(temp, left_on='STREET_ID', right_on='STREET_ID')

Thus getting the desired final data frame with associated street ID's