This question already has answers here:
How do I expand the output display to see more columns of a Pandas DataFrame?
(22 answers)
Closed 1 year ago.
I am a complete novice when it comes to Python so this might be badly explained.
I have a pandas dataframe with 2485 entries for years from 1960-2020. I want to know how many entries there are for each year, which I can easily get with the .value_counts() method. My issue is that when I print this, the output only shows me the top 5 and bottom 5 entries, rather than the number for every year. Is there a way to display all the value counts for all the years in the DataFrame?
Use pd.set_options and set display.max_rows to None:
>>> pd.set_option("display.max_rows", None)
Now you can display all rows of your dataframe.
Options and settings
pandas.set_option
If suppose the name of dataframe is 'df' then use
counts = df.year.value_counts()
counts.to_csv('name.csv',index=false)
As our terminal can't display entire columns they just display the top and bottom by collapsing the remaining values so try saving in a csv and see the records
Related
This question already has answers here:
How to avoid pandas creating an index in a saved csv
(6 answers)
Closed 5 months ago.
I'm using the pandas split function to create new columns from an existing one. All of that works fine and I get my expected columns created. The issue is that it is creating an additional column in the exported csv file. So, it says there are 3 columns when there are actually 4.
I've tried various functions to drop that column, but it isn't recognized as part of the data frame so it can't be successfully removed.
Hopefully someone has had this issue and can offer a possible solution.
[example of the csv data frame output with the unnecessary column added]
The column A doesn't come from split but it's the index of your actual dataframe by default. You can change that by setting index=False in df.to_csv:
df.to_csv('{PATH}.csv', index=False)
This question already has answers here:
How can repetitive rows of data be collected in a single row in pandas?
(3 answers)
pandas group by and find first non null value for all columns
(3 answers)
Closed 7 months ago.
While using iterrows to implement the logic takes lot of time.Can some suggest a way on how I could optimize the code with vectorized/apply()
Below is the input table..From a partition of (ITEMSALE,ITEMID),I need to populate rows with rank=1 .If any column value is null in rank=1,I need to populate the next available value in that column.This has to be done for all columns in dataset.
Below is the output format expected
I have tried below logic using iterrows where am accessing values rowise.Performance is too low using this method.
This should get you what you need
df.loc[df.loc[df['Item_ID'].isna()].groupby('Item_Sale')['Date'].idxmin()]
This question already has answers here:
Concatenate strings from several rows using Pandas groupby
(8 answers)
Closed 3 years ago.
I am fairly new to using Python and having come from using SQL I have been using PANDAS to build reports from CSV files with reasonable success. I have been able to answer most of questions thanks mainly to this site, but I dont seem to be able to find an answer to my question:
I have a dataframe which has 2 columns I want to be able to group on the first column and display the lowest and highest alphabetical values from the second column concatenated into a third column. I could do this fairly easy in SQL but as I say I am struggling getting my head around it in Python/Pandas
example:
source data:
LINK_NAME, CITY_NAME
Linka, Citya
Linka, Cityz
Linkb,Cityx
Linkb,Cityc
Desired output:
LINK_NAME,LINKID
Linka, CityaCityz
Linkb,CitycCityx
Edit:
Sorry for missing part of your question.
To sort the strings within each group alphabetically, you could define a function to apply to the grouped items:
def first_and_last_alpha(series):
sorted_series = series.sort_values()
return "".join([sorted_series.iloc[0], sorted_series.iloc[-1]])
df.groupby("LINK_NAME")["CITY_NAME"].apply(first_and_last_alpha)
Original:
Your question seems to be a duplicate of this one.
The same effect, with your data, is achieved by:
df.groupby("LINK_NAME")["CITY_NAME"].apply(lambda x: "".join(x))
where df is your pandas.Dataframe object
In future, it's good to provide a reproducible example, including anything you've attempted before posting. For example, the output from df.to_dict() would allow me to recreate your example data instantly.
This question already has answers here:
How do I select rows from a DataFrame based on column values?
(16 answers)
Closed 3 years ago.
This is the dataset that I am attempting to use:
https://storage.googleapis.com/hewwo/NCHS_-_Leading_Causes_of_Death__United_States.csv
I am wondering how I can specifically drop rows that contain certain values. In this example, many rows from the "Cause Name" column have values of "All causes". I want to drop any row that has this value for that column. This is what I have tried so far:
death2[death2['cause_name' ]!= 'All Causes']
While this did not give me any errors, it also did not seem to do anything to my dataset. Rows with "All causes" were still present. Am I doing something wrong?
No changes were made to your DataFrame. You need to reassign it if you want to change it.
death2 = death2[death2['cause_name' ]!= 'All Causes']
This question already has answers here:
How do I expand the output display to see more columns of a Pandas DataFrame?
(22 answers)
Closed 4 years ago.
I am trying to output all columns of a data frame .
Here is the code below:
df_advertiser_activity_part_qa = df_advertiser_activity_part.loc[(df_advertiser_activity_part['advertiser_id']==209988 )]
df_advertiser_activity_part_qa.sort(columns ='date_each_day_et')
df_advertiser_activity_part_qa
when I output the data frame not all columns gets displayed . This has 21 columns and between some columns there is just there dots "..." I am using ipython notebook . Is there a way by which this can be ignored.
try:
pandas.set_option('display.max_columns', None)
but depending how many columns you have this is not a good idea. The data is being abbreviated because you have too many columns to fit practically on the screen.
You might be better off saving to a .csv to inspect the data.
df.to_csv('myfile.csv')
or if you have lots of rows:
df.head(1000).to_csv('myfile.csv')