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In newer version of Pandas, inspired by the documentation: https://pandas.pydata.org/docs/user_guide/10min.html#viewing-data

df[df["colume_name"] == some_value] #Scalar, True/False.. df[df["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in brackets () and combining them with & and | (and/or). Like this:

df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

Other filters

pandas.notna(df["colume_name"]) == True #Not NaN df['colume_name'].str.contains("text") #Search for "text" df['colume_name'].str.lower().str.contains("text") #Search for "text", after convertering to lowercase 

In newer versions of Pandas, inspired by the documentation (Viewing data):

df[df["colume_name"] == some_value] #Scalar, True/False.. df[df["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in parentheses, (), and combining them with & and | (and/or). Like this:

df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

Other filters

pandas.notna(df["colume_name"]) == True # Not NaN df['colume_name'].str.contains("text") # Search for "text" df['colume_name'].str.lower().str.contains("text") # Search for "text", after converting  to lowercase 

In newer version of Pandas, inspired by the documentation: https://pandas.pydata.org/docs/user_guide/10min.html#viewing-data

df[df["colume_name"] == some_value] #Scalar, True/False.. df[df["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in brackets () and combining them with & and | (and/or). Like this:

df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

Other clauses

pandas.notna(df["colume_name"]) == True #Not NaN df['colume_name'].str.contains("text") #Search for "text" df['colume_name'].str.lower().str.contains("text") #Search for "text", after convertering to lowercase 

In newer version of Pandas, inspired by the documentation: https://pandas.pydata.org/docs/user_guide/10min.html#viewing-data

df[df["colume_name"] == some_value] #Scalar, True/False.. df[df["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in brackets () and combining them with & and | (and/or). Like this:

df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

Other filters

pandas.notna(df["colume_name"]) == True #Not NaN df['colume_name'].str.contains("text") #Search for "text" df['colume_name'].str.lower().str.contains("text") #Search for "text", after convertering to lowercase 

In newer version of Pandas, inspired by the documentation: https://pandas.pydata.org/docs/user_guide/10min.html#viewing-data

pd[pd["colume_name"] == some_value] #Scalar, True/False.. pd[pd["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in brackets () and combining them with & and | (and/or). Like this:

pd[(pd["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

In newer version of Pandas, inspired by the documentation: https://pandas.pydata.org/docs/user_guide/10min.html#viewing-data

df[df["colume_name"] == some_value] #Scalar, True/False.. df[df["colume_name"] == "some_value"] #String 

Combine multiple conditions by putting the clause in brackets () and combining them with & and | (and/or). Like this:

df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])] 

Other clauses

pandas.notna(df["colume_name"]) == True #Not NaN df['colume_name'].str.contains("text") #Search for "text" df['colume_name'].str.lower().str.contains("text") #Search for "text", after convertering to lowercase