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installing pandas
pip install pandas |
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pip install pandas
import pandas as pd |
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pd -> pandas
df-> dataframe |
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to read a file into a dataframe
df= pd.read_csv('filename') |
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look at the first 5 lines
df.head() |
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to describe df
df.describe() |
df.info() |
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to print all the column names
telecom_data.columns |
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to get the dimension of df
df.shape |
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sort
sorting can be done column wise - default is ascending df.sort_values(by='Total day charge') |
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df.sort_values(col1) |
Sort values by col1 in ascending order (use ascending =False for descending sort)
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df.sort_values([col1,col2],ascending=[True,False]) |
Sort values by col1 in ascending order then col2 in descending order
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Filtering
df[condition] #eg: df[df['col']>5] |
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df[df['col'] > 0.5] |
Rows where the column col is greater than 0.5
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df[(df[col] > 0.5) & (df[col] < 0.7)] |
Rows where 0.7 > col > 0.5
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NOTE
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df.merge(df2) gives you a copy of df merged with df2. you may save it to a new variable. ex df3=df.merge(df2) |
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if you want to merge df2 to df right away use inplace.
df.merge(df2,inplace=True) |
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to delete a row - [axis=0 means rows]
new_df = df.drop([2,3],axis = 0) #this drops the row with index 2,3 |
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to delete a column- [axis=1 means columns]
new_df = df.drop(['col1','col2'],axis = 0) #this drops the column with name col1 and col2 |
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create or edit a new column
df['new_colname'] = 5 #this creates a new new column with all values as 5 |
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create a new column
df['new_colname'] = [list of values] #this creates a new column with list of values assigned to each corresponding row |
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NOTE :
df['new_colname'] = [list of values] throws an error if the no of items in [list of values] doesn't match no of rows |
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create or edit a new row
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df.loc[index_of_row] = [list of items] |
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NOTE :
df.loc[index_of_row] = [list of items] throws an error if the no of items doesn't match no of rows |
df[col] |
Returns column with label col as Series
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df[[col1, col2]] |
Returns multiple columns as a new DataFrame
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Country Capital Population
1 Belgium Brussels 11190846 2 India New Delhi 1303171035 3 Brazil Brasilia 207847528 |
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df.iloc([0], [0]) --> 'Belgium' | s.iloc[0] | Selection by position (0th position on row and column) |
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df.loc([0], ['Country']) --> 'Belgium' |
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df.ix[2] -->Country Brazil Capital Brasilia Population 207847528 |
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df.ix[1, 'Capital'] --> 'New Delhi' |
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df.iloc[0,:] | select First row
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df.set_index('column_one')
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Change the index with a new column
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df.columns = ['new_col_name1','new_col_name2','new_col_name3']
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Rename columns
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pd.isnull()
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Checks for null Values, Returns Boolean Arrray
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pd.notnull()
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Opposite of pd.isnull()
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df.dropna()
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Drop all rows that contain null values
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df.dropna(axis=1)
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Drop all columns that contain null values
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df.dropna(axis=1,thresh=n)
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Drop all rows have have less than n non null
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df.fillna(x)
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Replace all null values with x
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df1.append(df2) |
Adds the rows in df1 to the end of
df2 (columns should be identical) |
pd.concat([df1, df2],axis=1) |
Adds the columns in
df1 to the end of df2 (rows should be identical) |
df1.join(df2,on=col1,how='inner') |
joins 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' |
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left = takes the index of left df
right =takes the index of left outer = union of both keys inner = intersection of both keys |
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