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Is there a Python pandas function for retrieving a specific value of a dataframe based on its content?

I've got multiple excels and I need a specific value but in each excel, the cell with the value changes position slightly. However, this value is always preceded by a generic description of it which remains constant in all excels.
I was wondering if there was a way to ask Python to grab the value to the right of the element containing the string "xxx".

try iterating over the excel files (I guess you loaded each as a separate pandas object?)
somehting like for df in [dataframe1, dataframe2...dataframeN].
Then you could pick the column you need (if the column stays constant), e.g. - df['columnX'] and find which index it has:
df.index[df['columnX']=="xxx"]. Maybe will make sense to add .tolist() at the end, so that if "xxx" is a value that repeats more than once, you get all occurances in alist.
The last step would be too take the index+1 to get the value you want.
Hope it was helpful.
In general I would highly suggest to be more specific in your questions and provide code / examples.

How to use approx_count_distinct to count distinct combinations of two columns in a Spark DataFrame?

I have a Spark DataFrame (sdf) where each row shows an IP visiting a URL. I want to count distinct IP-URL pairs in this data frame and the most straightforward solution is sdf.groupBy("ip", "url").count(). However, since the data frame has billions of rows, precise counts can take quite a while. I'm not particularly familiar with PySpark -- I tried replacing .count() with .approx_count_distinct(), which was syntactically incorrect.
I searched "how to use .approx_count_distinct() with groupBy()" and found this answer. However, the solution suggested there (something along those lines: sdf.groupby(["ip", "url"]).agg(F.approx_count_distinct(sdf.url).alias("distinct_count"))) doesn't seem to give me the counts that I want. The method .approx_count_distinct() can't take two columns as arguments, so I can't write sdf.agg(F.approx_count_distinct(sdf.ip, sdf.url).alias("distinct_count")), either.
My question is, is there a way to get .approx_count_distinct() to work on multiple columns and count distinct combinations of these columns? If not, is there another function that can do just that and what's an example usage of it?
Thank you so much for your help in advance!

Group with expressions and alias as needed. Lets try:
df.groupBy("ip", "url").agg(expr("approx_count_distinct(ip)").alias('ip_count'),expr("approx_count_distinct(url)").alias('url_count')).show()

Your code sdf.groupby(["ip", "url"]).agg(F.approx_count_distinct(sdf.url).alias("distinct_count")) will give a value of 1 to every group since you are counting the value of one of the grouping column; url.
If you want to count distinct of IP-URL pairs using approx_count_distinct function, you can compound them in an array then apply the function. It would be something like this
sdf.selectExpr("approx_count_distinct(array(ip, url)) as distinct_count")

Pandas dataframe finding the mean

I have a dataframe that looks like the attached image. I want to find the mean for every finalAward_band 'value'. I'm not sure how to do this.

str.contains can be used to perform either substring searches or regex based search. The search defaults to regex-based unless you explicitly disable it.
Sometimes regex search is not required, so specify regex=False to disable it.
#select all rows containing "finalAward_band"
df1[df1['col'].str.contains('finalAward_band', regex=False)]
# same as df1[df1['col'].str.contains('finalAward_band')] but faster.

Pandas add column to new data frame at associated string value?

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.

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).

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!)

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

python dataframe lowercase slicing

I have a dataframe. I want to slice it by checking if the value contains a string. For example, this code works:
data_df[data_df['column1'].str.contains('test')]
But I first want to set my column1 to be all lowercase first. So being the n00b that I am, I tried:
data_df[data_df['column1'].lower().str.contains('test')]
Of course the Python gods gave me no mercy and gave me an AttributeError. Any tips on how I can slice a dataframe based on a substring but first make everything into lowercase first?
I feel like the following post is very close to my answer but I can't get it to work exactly how I described up there:
Python pandas dataframe slicing, with if condition
Thanks Python pros!!!

Try using apply()
data_df[data_df['column1'].apply(str.lower).str.contains('test')]

You can drop the apply:
data_df[data_df['column1'].str.lower().str.contains('test')]