Python get number of rows in dataframe

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Python get number of rows in dataframe

In this article, we show how to get the number of rows and columns in a pandas dataframe object in Python. So let's say you imported data from a Microsoft Excel spreadsheet such as CSV file or even from just a plain text file. At times, you definitely may want to know how many rows and columns there are from this data that you are reading. You may have a program that automatically reads a CSV file and now outputs how many rows and columns that have been read. So how can we find out and get the number of rows and columns there are in a pandas dataframe object? We can do this using the shape attribute. The shape attribute displays how many rows and columns there are in a pandas dataframe object. The shape attribute returns the number of rows and columns as a tuple. The tuple is compose of 2 values, the rows as the first value and the columns as the second value. This is shown in the following code below. >>> import pandas as pd >>> from numpy.random import randn >>> dataframe1= pd.DataFrame(randn(4,3),['A','B','C','D',],['W','X','Y']) >>> dataframe1 W X Y A 0.014062 0.577280 0.996097 B 0.697442 -0.468701 0.309528 C -0.613273 1.481231 -0.052422 D 2.174674 0.180203 0.556121 >>> dataframe1.shape (4, 3) >>> #add another column to the dataframe >>> dataframe1['Z']=randn(4) >>> dataframe1 W X Y Z A 0.364479 0.023884 -1.214802 0.860813 B -0.797658 -1.345063 -1.924267 2.483650 C 1.171122 1.547930 -1.209815 1.576785 D -0.643122 0.037218 1.180524 -0.860149 >>> dataframe1.shape (4, 4) So let's now go over the code. So we first have to import the pandas module. We do this with the line, import pandas as pd. as pd means that we can reference the pandas module with pd instead of writing out the full pandas each time. We import rand from numpy.random, so that we can populate the DataFrame with random values. In other words, we won't need to manually create the values in the table. The randn function will populate it with random values. We create a variable, dataframe1, which we set equal to, pd.DataFrame(randn(4,3),['A','B','C','D',],['X','Y','Z']) This creates a DataFrame object with 4 rows and 3 columns. The rows are 'A', 'B', 'C', and 'D'. The columns are 'W', 'X', and 'Y'. After we output the dataframe1 object, we get the DataFrame object with all the rows and columns, which you can see above. We then get the number of rows and columns in the dataframe object using the shape attribute. Since there are 4 rows and 3 columns, the tuple of (4,3) is returned. Next, to just show you that this changes if the dataframe changes, we add another column to the dataframe. This makes the dataframe have 4 columns and 4 rows. Now when we have the statement, dataframe1.shape, the tuple of (4,4) is returned. So this is show we can get the number of rows and columns in a pandas dataframe object in Python. Related Resources How to Randomly Select From or Shuffle a List in Python Counting number of Values in a Row or Columns is important to know the Frequency or Occurrence of your data. In this post we will see how we to use Pandas Count() and Value_Counts() functions Let's create a dataframe first with three columns A,B and C and values randomly filled with any integer between 0 and 5 inclusive import numpy as np import pandas as pd df = pd.DataFrame(np.random.randint(0, 5, (5, 3)), columns= ["A", "B","C"]) df.replace(1,np.nan,inplace=True) Pandas Count Number of Rows and Columns First find out the shape of dataframe i.e. number of rows and columns in this dataframe (5, 3) Here 5 is the number of rows and 3 is the number of columns Pandas Count Values for each Column We will use dataframe count() function to count the number of Non Null values in the dataframe. We will select axis =0 to count the values in each Column You can count the non NaN values in the above dataframe and match the values with this output Pandas Count Values for each row Change the axis = 1 in the count() function to count the values in each row. All None, NaN, NaT values will be ignored 0 3 1 3 2 3 3 2 4 1 dtype: int64 Pandas Count Along a level in multi-index Now we will see how Count() function works with Multi-Index dataframe and find the count for each level Let's create a Multi-Index dataframe with Name and Age as Index and Column as Salary idx = pd.MultiIndex.from_tuples([('Chris',48), ('Brian',np.nan), ('David',65),('Chris',34),('John',28)], names=['Name', 'Age']) col = ['Salary'] df = pd.DataFrame([120000, 140000, 90000, 101000, 59000], idx, col) df In this Multi-Index we will find the Count of Age and Salary for level Name You can set the level parameter as column "Name" and it will show the count of each Name Age and Salary Brian's Age is missing in the above dataframe that's the reason you see his Age as 0 i.e. No value available for his age but his Salary is present so Count is 1 Pandas Count Groupby You can also do a group by on Name column and use count function to aggregate the data and find out the count of the Names in the above Multi-Index Dataframe function Note: You have to first reset_index() to remove the multi-index in the above dataframe df.groupby(by='Name').agg('count') Alternatively, we can also use the count() method of pandas groupby to compute count of group excluding missing values df.groupby(by='Name').count() if you want to write the frequency back to the original dataframe then use transform() method. You can learn more about transform here. df['freq']=df.groupby(by='Name')['Name'].transform('count') df Pandas Count rows with Values There is another function called value_counts() which returns a series containing count of unique values in a Series or Dataframe Columns Let's take the above case to find the unique Name counts in the dataframe #value counts # Remove the multi-index using reset_index() in the above dataframe df=df.reset_index() df['Name'].value_counts() Chris 2 John 1 Brian 1 David 1 Name: Name, dtype: int64 Sort by Frequency You can also sort the count using the sort parameter #sort by frequency df['Name'].value_counts(sort=True) Chris 2 John 1 David 1 Brian 1 Name: Name, dtype: int64 Sort by Ascending Order Sort the frequencies in Ascending order # sort by ascending df['Name'].value_counts(sort=True, ascending=True) David 1 Brian 1 John 1 Chris 2 Name: Name, dtype: int64 Value Counts Percentage or Relative Count You can also get the relative frequency or percentage of each unique values using normalize parameters # Relative counts - find percentage df['Name'].value_counts(normalize=True) Chris 0.4 John 0.2 Brian 0.2 David 0.2 Name: Name, dtype: float64 Now Chris is 40% of all the values and rest of the Names are 20% each Binning Rather than counting you can also put these values into bins using the bins parameter This works only for Numeric data df['Salary'].value_counts(bins=2) (99500.0, 140000.0] 3 (58918.999, 99500.0] 2 Name: Salary, dtype: int64 Pandas Value Count for Multiple Columns value_counts() method can be applied only to series but what if you want to get the unique value count for multiple columns? No need to worry, You can use apply() to get the count for each of the column using value_counts() Let's create a new dataframe df = pd.DataFrame(np.random.randint(0, 2, (5, 3)), columns=["A", "B","C"]) df Apply pd.Series.value_counts to all the columns of the dataframe, it will give you the count of unique values for each row df.apply(pd.Series.value_counts, axis=1) Now change the axis to 0 and see what result you get, It gives you the count of unique values for each column df.apply(pd.Series.value_counts, axis=0) Alternatively, you can also use melt() to Unpivot a DataFrame from wide to long format and crosstab() to count the values for each column df1 = df.melt(var_name='columns', value_name='values') pd.crosstab(index=df1['values'], columns=df1['columns']) Pandas Count Specific Values in Column You can also get the count of a specific value in dataframe by boolean indexing and sum the corresponding rows If you see clearly it matches the last row of the above result i.e. count of value 1 in each column A 3.0 B 1.0 C 2.0 dtype: float64 Pandas Count Specific Values in rows Now change the axis to 1 to get the count of columns with value 1 in a row You can see the first row has only 2 columns with value 1 and similarly count for 1 follows for other rows #By row df[df == 1].sum(axis=1) 0 2.0 1 2.0 2 3.0 3 2.0 4 2.0 dtype: float64 Conclusion Finally we have reached to the end of this post and just to summarize what we have learnt in the following lines: Pandas count value for each row and columns using the dataframe count() function Count for each level in a multi-index dataframe Pandas value_counts() method to find frequency of unique values in a series How to apply value_counts on multiple columns Count a Specific value in a dataframe rows and columns if you know any other methods which can be used for computing frequency or counting values in Dataframe then please share that in the comments section below Data can be messy: it often comes from various sources, doesn't have structure or contains errors and missing fields. Working with data requires to clean, refine and filter the dataset before making use of it. Pandas is one of the most popular tools to perform such data transformations. It is an open source library for Python offering a simple way to aggregate, filter and analyze data. The library is often used together with Jupyter notebooks to empower data exploration in various research and data visualization projects. Pandas introduces the concept of a DataFrame ? a table-like data structure similar to a spreadsheet. You can import data in a data frame, join frames together, filter rows and columns and export the results in various file formats. Here is a pandas cheat sheet of the most common data operations: Getting Started Import Pandas & Numpy import numpy as np import pandas as pd Get the first 5 rows in a dataframe: df.head(5) Get the last 5 rows in a dataframe: df.tail(5) Import Data Create DataFrame from dictionary: df = pd.DataFrame.from_dict({ 'company': 'Pandology', 'metrics': [[{'score': 10}, {'score': 20}, {'score': 35}]] }) Import data from a CSV file: df = pd.read_csv('data/my-data.csv') Import data from an Excel Spreadsheet: df_excel = pd.read_excel('./data/spreadsheet.xls', sheetname='sheet1', skiprows=[1] # header data ) Import data from an Excel Spreadsheet without the header: df_names = pd.read_excel('./data/names_all.xls', header=None) Export Data Export as an Excel Spreadsheet: df[['name_company', 'name_candidate']].to_excel('./output/companies.xls') Export to a CSV file: df.to_csv('data-output/my-data.csv') Convert Data Types Convert column data to string: df['name'] = df['name'].astype('str') Convert column data to integer (nan values are set to -1): df['col'] = df['col'].fillna(-1).astype(int) Convert column data to numeric type: df['col'] = pd.to_numeric(df['col']) Get / Set Values Get the value of a column on a row with index idx: df.get_value(idx, 'col_name') Set column value on a given row: idx = df[df['address'] == '4th Avenue'].index df.set_value(idx, 'id', '502') Count Number of rows in a DataFrame: len(df) Count rows where column is equal to a value: len(df[df['score'] == 1.0]) Count unique values in a column: df['name'].nunique() Count rows based on a value: df['is_removed'].value_counts() # Count null values as well: df['is_removed'].value_counts(dropna=False) Filter Data Filter rows based on a value: df[df['id'] == '48'] Filter rows based on multiple values: df[(df['category'] == 'national') & (df['is_removed'] == '1')] Filter rows that contain a string: df[df['category'].str.contains('national')] Filter rows containing some of the strings: df['address'].str.contains('|'.join(['4th Avenue', 'Broadway'])) Filter rows where value is in a list: df[df['id'].isin(['109', '673'])] Filter rows where value is _not_ in a list: df = df[~df['id'].isin(['1', '2', '3'])] Filter all rows that have valid values (not null): df = df[pd.notnull(df['latitude'])] Sort Data Sort rows by value: df.sort_values('nom', inplace=True) Sort Columns By Name: df = df.reindex_axis(sorted(df.columns), axis=1) Rename columns Rename particular columns: df.rename(columns={'id': 'id_new', 'object': 'object_new'}, inplace=True) Rename all columns: df.columns = ['id', 'object', 'address', 'type', 'category'] Make all columns lowercase: df.columns = map(str.lower, df.columns) Drop data Drop column named col df = df.drop('col', axis=1) Drop all rows with null index: df[pd.notnull(df.index)] Drop rows that have missing values in some columns: df.dropna(subset=['plsnkv', 'plsnnum']) Drop duplicate rows: df = df.drop_duplicates(subset='id', keep='first') Create columns Create a new column based on row data: def cad_id(row): return row['region'] + '.' + row['lot'] + '.' + row['building'] df['cad_id'] = df.apply(cad_id, axis=1) Create a new column based on another column: df['is_removed'] = df['object'].map(lambda x: 1 if 'removed' in x else 0) Create multiple new columns based on row data: def geocode(row): address = api_geocode(street=row['street'], house_number=row['building'], zip_code=row['zip']) return pd.Series([address.get('lat'), address.get('lon'), address.get('borough')]) df[['lat', 'lon', 'borough']] = df.apply(geocode, axis=1) Match id to label: def zone_label(zone_id): return { 'C': 'City Center', 'S': 'Suburbs', 'R': 'Residential District' }.get(zone_id, zone_id) df['zone_label'] = df['zone_id'].map(zone_label) Data Joins Join data frames by columns: df_lots.merge(df_buildings, on= ['lot_id', 'mun_id'], suffixes=('_lot', '_building')) Concatenate two data frames (one after the other): df_all = pd.concat([df_first, df_second]) Utilities Increase the number of table rows & columns shown: pd.options.display.max_rows = 999 pd.options.display.max_columns = 999 Learn More We are covering data analysis and visualization in our upcoming course "Data & the City". The course will discuss how to collect, store and visualize urban data in a useful way. Subscribe bellow and we'll notify you when the course becomes available. Subscribe here

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