Python panda count rows

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Python panda count rows

3.0.0 Dec 5, 2020 2.0.0 May 16, 2020 1.1.0 Feb 13, 2020 1.0.0 Jan 13, 2020 0.14.0 Oct 19, 2019 0.13.0 Mar 8, 2019 0.12.1 Sep 1, 2017 0.12.0 Aug 27, 2017 0.11.5 Jun 13, 2017 0.11.4 Jan 23, 2017 0.11.3 Dec 5, 2016 0.11.2 Feb 16, 2016 0.11.1 Feb 7, 2016 0.11.0 Feb 7, 2016 0.10.0 May 27, 2014 0.9.11 Jun 30, 2011 0.9.10 Jun 23, 2011 0.9.9 Jun 22, 2011 0.9.8 May 23, 2011 0.9.7 May 13, 2011 0.9.6 May 12, 2011 0.9.5 Mar 29, 2011 0.9.4 May 11, 2011 0.9.3 May 11, 2011 0.9.2 May 11, 2011 0.9.1 May 11, 2011 0.9.0 May 11, 2011 0.8.4 May 11, 2011 0.8.3 May 11, 2011 0.8.2 May 11, 2011 0.8.1 May 11, 2011 0.8.0 May 11, 2011 0.7.1 May 11, 2011 0.7.0 May 11, 2011 0.6.4 May 11, 2011 0.6.3 May 11, 2011 0.6.2 May 11, 2011 0.6.1 May 11, 2011 0.0.1 Sep 8, 2010 You can create subset of data with your condition and then use shape or len: print df col1 education 0 a 9th 1 b 9th 2 c 8th print df.education == '9th' 0 True 1 True 2 False Name: education, dtype: bool print df[df.education == '9th'] col1 education 0 a 9th 1 b 9th print df[df.education == '9th'].shape[0] 2 print len(df[df['education'] == '9th']) 2 Performance is interesting, the fastest solution is compare numpy array and sum: Code: import perfplot, string np.random.seed(123) def shape(df): return df[df.education == 'a'].shape[0] def len_df(df): return len(df[df['education'] == 'a']) def query_count(df): return df.query('education == "a"').education.count() def sum_mask(df): return (df.education == 'a').sum() def sum_mask_numpy(df): return (df.education.values == 'a').sum() def make_df(n): L = list(string.ascii_letters) df = pd.DataFrame(np.random.choice(L, size=n), columns=['education']) return df perfplot.show( setup=make_df, kernels=[shape, len_df, query_count, sum_mask, sum_mask_numpy], n_range=[2**k for k in range(2, 25)], logx=True, logy=True, equality_check=False, xlabel='len(df)') If you're interested in working with data in Python, you're almost certainly going to be using the pandas library. But even when you've learned pandas -- perhaps in our interactive pandas course -- it's easy to forget the specific syntax for doing something. That's why we've created a pandas cheat sheet to help you easily reference the most common pandas tasks.Before we dive into the cheat sheet, it's worth mentioning that you shouldn't rely on just this. If you haven't learned any pandas yet, we'd strongly recommend working through our pandas course. This cheat sheet will help you quickly find and recall things you've already learned about pandas; it isn't designed to teach you pandas from scratch!It's also a good idea to check to the official pandas documentation from time to time, even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs, and the documentation goes into a lot more detail than we can fit in a single sheet anyway!If you're looking to use pandas for a specific task, we also recommend checking out the full list of our free Python tutorials; many of them make use of pandas in addition to other Python libraries. In our Python datetime tutorial, for example, you'll also learn how to work with dates and times in pandas.Pandas Cheat Sheet: GuideFirst, it may be a good idea to bookmark this page, which will be easy to search with Ctrl+F when you're looking for something specific. However, we've also created a PDF version of this cheat sheet that you can download from here in case you'd like to print it out.In this cheat sheet, we'll use the following shorthand:df | Any pandas DataFrame objects | Any pandas Series object As you scroll down, you'll see we've organized related commands using subheadings so that you can quickly search for and find the correct syntax based on the task you're trying to complete.Also, a quick reminder -- to make use of the commands listed below, you'll need to first import the relevant libraries like so:import pandas as pdimport numpy as npImporting DataUse these commands to import data from a variety of different sources and formats.pd.read_csv(filename) | From a CSV filepd.read_table(filename) | From a delimited text file (like TSV)pd.read_excel(filename) | From an Excel filepd.read_sql(query, connection_object) | Read from a SQL table/databasepd.read_json(json_string) | Read from a JSON formatted string, URL or file.pd.read_html(url) | Parses an html URL, string or file and extracts tables to a list of dataframespd.read_clipboard() | Takes the contents of your clipboard and passes it to read_table()pd.DataFrame(dict) | From a dict, keys for columns names, values for data as listsExporting DataUse these commands to export a DataFrame to CSV, .xlsx, SQL, or JSON.df.to_csv(filename) | Write to a CSV filedf.to_excel(filename) | Write to an Excel filedf.to_sql(table_name, connection_object) | Write to a SQL tabledf.to_json(filename) | Write to a file in JSON formatCreate Test ObjectsThese commands can be useful for creating test segments.pd.DataFrame(np.random.rand(20,5)) | 5 columns and 20 rows of random floatspd.Series(my_list) | Create a series from an iterable my_listdf.index = pd.date_range('1900/1/30', periods=df.shape[0]) | Add a date indexViewing/Inspecting DataUse these commands to take a look at specific sections of your pandas DataFrame or Series.df.head(n) | First n rows of the DataFramedf.tail(n) | Last n rows of the DataFramedf.shape | Number of rows and () | Index, Datatype and Memory informationdf.describe() | Summary statistics for numerical columnss.value_counts(dropna=False) | View unique values and countsdf.apply(pd.Series.value_counts) | Unique values and counts for all columnsSelectionUse these commands to select a specific subset of your data.df[col] | Returns column with label col as Seriesdf[[col1, col2]] | Returns columns as a new DataFrames.iloc[0] | Selection by positions.loc['index_one'] | Selection by indexdf.iloc[0,:] | First rowdf.iloc[0,0] | First element of first columnData CleaningUse these commands to perform a variety of data cleaning tasks.df.columns = ['a','b','c'] | Rename columnspd.isnull() | Checks for null Values, Returns Boolean Arrraypd.notnull() | Opposite of pd.isnull()df.dropna() | Drop all rows that contain null valuesdf.dropna(axis=1) | Drop all columns that contain null valuesdf.dropna(axis=1,thresh=n) | Drop all rows have have less than n non null valuesdf.fillna(x) | Replace all null values with xs.fillna(s.mean()) | Replace all null values with the mean (mean can be replaced with almost any function from the statistics module)s.astype(float) | Convert the datatype of the series to floats.replace(1,'one') | Replace all values equal to 1 with 'one's.replace([1,3],['one','three']) | Replace all 1 with 'one' and 3 with 'three'df.rename(columns=lambda x: x + 1) | Mass renaming of columnsdf.rename(columns={'old_name': 'new_ name'}) | Selective renamingdf.set_index('column_one') | Change the indexdf.rename(index=lambda x: x + 1) | Mass renaming of indexFilter, Sort, and GroupbyUse these commands to filter, sort, and group your data.df[df[col] > 0.5] | Rows where the column col is greater than 0.5df[(df[col] > 0.5) & (df[col] < 0.7)] | Rows where 0.7 > col > 0.5df.sort_values(col1) | Sort values by col1 in ascending orderdf.sort_values(col2,ascending=False) | Sort values by col2 in descending orderdf.sort_values([col1,col2],ascending=[True,False]) | Sort values by col1 in ascending order then col2 in descending orderdf.groupby(col) | Returns a groupby object for values from one columndf.groupby([col1,col2]) | Returns groupby object for values from multiple columnsdf.groupby(col1)[col2] | Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics module)df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) | Create a pivot table that groups by col1 and calculates the mean of col2 and col3df.groupby(col1).agg(np.mean) | Find the average across all columns for every unique col1 groupdf.apply(np.mean) | Apply the function np.mean() across each columnnf.apply(np.max,axis=1) | Apply the function np.max() across each rowJoin/CombineUse these commands to combine multiple dataframes into a single one.df1.append(df2) | Add the rows in df1 to the end of df2 (columns should be identical)pd.concat([df1, df2],axis=1) | Add the columns in df1 to the end of df2 (rows should be identical)df1.join(df2,on=col1,how='inner') | SQL-style join the columns in df1 with the columns on df2 where the rows for col have identical values. 'how' can be one of 'left', 'right', 'outer', 'inner'StatisticsUse these commands to perform various statistical tests. (These can all be applied to a series as well.)df.describe() | Summary statistics for numerical columnsdf.mean() | Returns the mean of all columnsdf.corr() | Returns the correlation between columns in a DataFramedf.count() | Returns the number of non-null values in each DataFrame columndf.max() | Returns the highest value in each columndf.min() | Returns the lowest value in each columndf.median() | Returns the median of each columndf.std() | Returns the standard deviation of each columnDownload a printable version of this cheat sheetIf you'd like to download a printable version of this cheat sheet you can do so here. python panda dataframe count rows

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